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It has been hypothe-" }, { "category_id": 15, "poly": [ 393.0, 1284.0, 1304.0, 1284.0, 1304.0, 1318.0, 393.0, 1318.0 ], "score": 0.987, "text": " sized that this is a consequence of implicit multitask learning in language models'" }, { "category_id": 15, "poly": [ 393.0, 1314.0, 1304.0, 1314.0, 1304.0, 1349.0, 393.0, 1349.0 ], "score": 0.974, "text": "pretraining (Radford et al., 2O19). Can zero-shot generalization instead be directly" }, { "category_id": 15, "poly": [ 394.0, 1346.0, 1304.0, 1346.0, 1304.0, 1380.0, 394.0, 1380.0 ], "score": 0.988, "text": "induced by explicit multitask learning? To test this question at scale,we develop" }, { "category_id": 15, "poly": [ 392.0, 1377.0, 1306.0, 1377.0, 1306.0, 1408.0, 392.0, 1408.0 ], "score": 0.993, "text": "a system for easily mapping any natural language tasks into a human-readable" }, { "category_id": 15, "poly": [ 393.0, 1407.0, 1306.0, 1407.0, 1306.0, 1439.0, 393.0, 1439.0 ], "score": 0.988, "text": "prompted form.We convert a large set of supervised datasets, each with multiple" }, { "category_id": 15, "poly": [ 393.0, 1437.0, 1306.0, 1437.0, 1306.0, 1472.0, 393.0, 1472.0 ], "score": 0.988, "text": "prompts with diverse wording.These prompted datasets allow for benchmarking" }, { "category_id": 15, "poly": [ 394.0, 1469.0, 1306.0, 1469.0, 1306.0, 1501.0, 394.0, 1501.0 ], "score": 0.981, "text": "the ability of a model to perform completely unseen tasks.We fine-tune a pre-" }, { "category_id": 15, "poly": [ 394.0, 1499.0, 1305.0, 1499.0, 1305.0, 1530.0, 394.0, 1530.0 ], "score": 0.96, "text": "trained encoder-decoder model (Raffel et al.,2O2O; Lester et al.,2021) on this mul-" }, { "category_id": 15, "poly": [ 394.0, 1528.0, 1307.0, 1528.0, 1307.0, 1563.0, 394.0, 1563.0 ], "score": 0.991, "text": "titask mixture covering a wide variety of tasks.The model attains strong zero-shot" }, { "category_id": 15, "poly": [ 395.0, 1561.0, 1247.0, 1561.0, 1247.0, 1592.0, 395.0, 1592.0 ], "score": 0.989, "text": "performance on several standard datasets,often outperforming models up to" }, { "category_id": 15, "poly": [ 393.0, 1589.0, 1306.0, 1589.0, 1306.0, 1624.0, 393.0, 1624.0 ], "score": 0.981, "text": " its size.Further, our approach attains strong performance on a subset of tasks from" }, { "category_id": 15, "poly": [ 394.0, 1620.0, 1035.0, 1620.0, 1035.0, 1654.0, 394.0, 1654.0 ], "score": 0.992, "text": "the BIG-bench benchmark,outperforming models up to" }, { "category_id": 15, "poly": [ 1077.0, 1620.0, 1306.0, 1620.0, 1306.0, 1654.0, 1077.0, 1654.0 ], "score": 0.978, "text": "its size.All trained" }, { "category_id": 15, "poly": [ 394.0, 1651.0, 1304.0, 1651.0, 1304.0, 1683.0, 394.0, 1683.0 ], "score": 0.993, "text": "models are available at https://github.com/bigscience-workshop/t-zero,and all" }, { "category_id": 15, "poly": [ 393.0, 1680.0, 1288.0, 1680.0, 1288.0, 1718.0, 393.0, 1718.0 ], "score": 0.999, "text": "prompts are available at https://github.com/bigscience-workshop/promptsource." }, { "category_id": 15, "poly": [ 295.0, 1835.0, 1404.0, 1835.0, 1404.0, 1868.0, 295.0, 1868.0 ], "score": 0.992, "text": "Recent work has shown that large language models exhibit the ability to perform reasonable zero-" }, { "category_id": 15, "poly": [ 294.0, 1862.0, 1404.0, 1862.0, 1404.0, 1900.0, 294.0, 1900.0 ], "score": 0.975, "text": " shot generalization to new tasks (Brown et al.,2O2O; Kim et al.,2021).Despite being trained on only" }, { "category_id": 15, "poly": [ 294.0, 1895.0, 1406.0, 1895.0, 1406.0, 1932.0, 294.0, 1932.0 ], "score": 0.993, "text": "language modeling objectives, these models can perform relatively well at new tasks that they have" }, { "category_id": 15, "poly": [ 293.0, 1920.0, 1407.0, 1920.0, 1407.0, 1967.0, 293.0, 1967.0 ], "score": 0.982, "text": " not been explicitly trained to perform, for instance answering a question on a passage or performing" }, { "category_id": 15, "poly": [ 293.0, 1955.0, 1407.0, 1955.0, 1407.0, 1994.0, 293.0, 1994.0 ], "score": 0.978, "text": " summarization. An influential hypothesis is that large language models generalize to new tasks as a" }, { "category_id": 15, "poly": [ 570.0, 375.0, 764.0, 375.0, 764.0, 407.0, 570.0, 407.0 ], "score": 0.996, "text": "Albert Webson*" }, { "category_id": 15, "poly": [ 567.0, 403.0, 776.0, 403.0, 776.0, 442.0, 567.0, 442.0 ], "score": 1.0, "text": "Brown University" }, { "category_id": 15, "poly": [ 312.0, 373.0, 472.0, 373.0, 472.0, 409.0, 312.0, 409.0 ], "score": 0.996, "text": "Victor Sanh*" }, { "category_id": 15, "poly": [ 311.0, 403.0, 478.0, 403.0, 478.0, 442.0, 311.0, 442.0 ], "score": 1.0, "text": "Hugging Face" }, { "category_id": 15, "poly": [ 710.0, 863.0, 830.0, 863.0, 830.0, 895.0, 710.0, 895.0 ], "score": 0.998, "text": "Jos Rozen" }, { "category_id": 15, "poly": [ 710.0, 893.0, 915.0, 893.0, 915.0, 928.0, 710.0, 928.0 ], "score": 0.994, "text": "Naver Labs Europe" }, { "category_id": 15, "poly": [ 877.0, 374.0, 1039.0, 374.0, 1039.0, 407.0, 877.0, 407.0 ], "score": 0.963, "text": "Colin 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348.0, 650.0, 348.0 ], "score": 0.943, "text": "On a scale of 1" }, { "category_id": 15, "poly": [ 468.0, 341.0, 743.0, 341.0, 743.0, 368.0, 468.0, 368.0 ], "score": 0.953, "text": "to 5,I would give this a" }, { "category_id": 15, "poly": [ 1200.0, 373.0, 1218.0, 373.0, 1218.0, 392.0, 1200.0, 392.0 ], "score": 0.999, "text": "4" }, { "category_id": 15, "poly": [ 516.0, 386.0, 692.0, 386.0, 692.0, 415.0, 516.0, 415.0 ], "score": 0.997, "text": "Question Answering" }, { "category_id": 15, "poly": [ 922.0, 383.0, 1055.0, 383.0, 1055.0, 485.0, 922.0, 485.0 ], "score": 0.944, "text": "T" }, { "category_id": 15, "poly": [ 391.0, 419.0, 818.0, 419.0, 818.0, 447.0, 391.0, 447.0 ], "score": 0.948, "text": "I know that the answer to “What team did" }, { "category_id": 15, "poly": [ 390.0, 439.0, 687.0, 439.0, 687.0, 493.0, 390.0, 493.0 ], "score": 0.711, "text": "thnedthegtisin" }, { "category_id": 15, "poly": [ 754.0, 439.0, 819.0, 439.0, 819.0, 493.0, 754.0, 493.0 ], "score": 0.827, "text": "nthers" }, { "category_id": 15, "poly": [ 1124.0, 452.0, 1309.0, 452.0, 1309.0, 479.0, 1124.0, 479.0 ], "score": 0.998, "text": "Arizona Cardinals" }, { "category_id": 15, "poly": [ 476.0, 484.0, 731.0, 484.0, 731.0, 508.0, 476.0, 508.0 ], "score": 0.974, "text": "you teli me what it is?" }, { "category_id": 15, "poly": [ 360.0, 525.0, 519.0, 525.0, 519.0, 560.0, 360.0, 560.0 ], "score": 0.992, "text": "Multi-task training" }, { "category_id": 15, "poly": [ 357.0, 557.0, 574.0, 557.0, 574.0, 589.0, 357.0, 589.0 ], "score": 0.99, "text": "Zero-shot generalization" }, { "category_id": 15, "poly": [ 486.0, 593.0, 723.0, 593.0, 723.0, 617.0, 486.0, 617.0 ], "score": 0.983, "text": "Natural Language Inference" }, { "category_id": 15, "poly": [ 369.0, 624.0, 837.0, 624.0, 837.0, 654.0, 369.0, 654.0 ], "score": 0.963, "text": "Suppose “The banker contacted the professors" }, { "category_id": 15, "poly": [ 389.0, 644.0, 819.0, 644.0, 819.0, 676.0, 389.0, 676.0 ], "score": 0.929, "text": "andthe athlete\".Can weinferthat \"The" }, { "category_id": 15, "poly": [ 1187.0, 646.0, 1231.0, 646.0, 1231.0, 675.0, 1187.0, 675.0 ], "score": 0.997, "text": "Yes" }, { "category_id": 15, "poly": [ 430.0, 669.0, 777.0, 669.0, 777.0, 693.0, 430.0, 693.0 ], "score": 0.975, "text": "banker contacted the professors\"?" }, { "category_id": 15, "poly": [ 295.0, 736.0, 1405.0, 736.0, 1405.0, 772.0, 295.0, 772.0 ], "score": 0.987, "text": "Figure 1: Our model and prompt format. TO is an encoder-decoder model that consumes textual" }, { "category_id": 15, "poly": [ 294.0, 768.0, 1404.0, 768.0, 1404.0, 802.0, 294.0, 802.0 ], "score": 0.993, "text": "inputs and produces target responses. It is trained on a multitask mixture of NLP datasets parti-" }, { "category_id": 15, "poly": [ 295.0, 797.0, 1405.0, 797.0, 1405.0, 832.0, 295.0, 832.0 ], "score": 0.991, "text": "tioned into diferent tasks.Each dataset is associated with multiple prompt templates that are used" }, { "category_id": 15, "poly": [ 294.0, 828.0, 1406.0, 828.0, 1406.0, 862.0, 294.0, 862.0 ], "score": 0.989, "text": "to format example instances to input and target pairs.Italics indicate the inserted fields from the raw" }, { "category_id": 15, "poly": [ 295.0, 859.0, 1406.0, 859.0, 1406.0, 895.0, 295.0, 895.0 ], "score": 0.982, "text": "example data. After training on a diverse mixture of tasks (top),our model is evaluated on zero-shot" }, { "category_id": 15, "poly": [ 293.0, 890.0, 1021.0, 890.0, 1021.0, 924.0, 293.0, 924.0 ], "score": 0.987, "text": " generalization to tasks that are not seen during training (botom)." }, { "category_id": 15, "poly": [ 332.0, 1973.0, 1404.0, 1973.0, 1404.0, 2011.0, 332.0, 2011.0 ], "score": 0.989, "text": "lFor example， https://www.quizbreaker.com/trivia-questions, htps://www.scarymommy.com/best-trivia-" }, { "category_id": 15, "poly": [ 295.0, 2005.0, 1154.0, 2005.0, 1154.0, 2035.0, 295.0, 2035.0 ], "score": 0.995, "text": "questions-answers/,and https://parade.com/944584/parade/trivia-questions-for-kids/." }, { "category_id": 15, "poly": [ 295.0, 72.0, 818.0, 72.0, 818.0, 110.0, 295.0, 110.0 ], "score": 0.989, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 838.0, 2085.0, 862.0, 2085.0, 862.0, 2120.0, 838.0, 2120.0 ], "score": 1.0, "text": "2" }, { "category_id": 15, "poly": [ 292.0, 951.0, 1404.0, 951.0, 1404.0, 993.0, 292.0, 993.0 ], "score": 0.977, "text": " result of an implicit process of multitask learning (Radford et al., 2019). As a byproduct of learning" }, { "category_id": 15, "poly": [ 293.0, 987.0, 1405.0, 987.0, 1405.0, 1018.0, 293.0, 1018.0 ], "score": 0.989, "text": " to predict the next word,a language model is forced to learn from a mixture of implicit tasks included" }, { "category_id": 15, "poly": [ 294.0, 1017.0, 1405.0, 1017.0, 1405.0, 1051.0, 294.0, 1051.0 ], "score": 0.995, "text": "in their pretraining corpus.For example,by training on generic text from a web forum,a model" }, { "category_id": 15, "poly": [ 292.0, 1045.0, 1407.0, 1045.0, 1407.0, 1085.0, 292.0, 1085.0 ], "score": 0.987, "text": " might implicitly learn the format and structure of question answering. This gives large language" }, { "category_id": 15, "poly": [ 292.0, 1075.0, 1407.0, 1075.0, 1407.0, 1115.0, 292.0, 1115.0 ], "score": 0.989, "text": " models the ability to generalize to unseen tasks presented with natural language prompts, going" }, { "category_id": 15, "poly": [ 294.0, 1108.0, 1405.0, 1108.0, 1405.0, 1142.0, 294.0, 1142.0 ], "score": 0.985, "text": "beyond prior multitask studies on generalization to unseen datasets (Khashabi et al.,2O20a; Ye" }, { "category_id": 15, "poly": [ 292.0, 1136.0, 1407.0, 1136.0, 1407.0, 1176.0, 292.0, 1176.0 ], "score": 0.982, "text": "et al., 2021). However, this ability requires a sufficiently large model and is sensitive to the wording" }, { "category_id": 15, "poly": [ 292.0, 1166.0, 1237.0, 1166.0, 1237.0, 1204.0, 292.0, 1204.0 ], "score": 0.984, "text": "of its prompts (Perez et al.,2021; Zhao et al.,2021; Reynolds and McDonell, 2021)." }, { "category_id": 15, "poly": [ 292.0, 1443.0, 1404.0, 1443.0, 1404.0, 1482.0, 292.0, 1482.0 ], "score": 0.989, "text": " In this paper, we focus on explicitly training language models in a supervised and massively multi-" }, { "category_id": 15, "poly": [ 295.0, 1476.0, 1405.0, 1476.0, 1405.0, 1511.0, 295.0, 1511.0 ], "score": 0.984, "text": "task fashion. Our approach uses a training mixture consisting of a large set of different tasks specified" }, { "category_id": 15, "poly": [ 293.0, 1506.0, 1401.0, 1506.0, 1401.0, 1542.0, 293.0, 1542.0 ], "score": 0.993, "text": " in natural language prompts. Our goal is to induce a model to better generalize to unseen tasks with-" }, { "category_id": 15, "poly": [ 293.0, 1537.0, 1404.0, 1537.0, 1404.0, 1572.0, 293.0, 1572.0 ], "score": 0.993, "text": "out requiring massive scale,as well as being more robust to the wording choices of the prompts." }, { "category_id": 15, "poly": [ 292.0, 1566.0, 1404.0, 1566.0, 1404.0, 1604.0, 292.0, 1604.0 ], "score": 0.991, "text": " To convert a large set of natural language tasks into prompted form, we use a simple templating" }, { "category_id": 15, "poly": [ 295.0, 1597.0, 1405.0, 1597.0, 1405.0, 1633.0, 295.0, 1633.0 ], "score": 0.991, "text": "language for structured datasets. We develop an interface for prompt collection from public contrib-" }, { "category_id": 15, "poly": [ 296.0, 1629.0, 1405.0, 1629.0, 1405.0, 1661.0, 296.0, 1661.0 ], "score": 0.988, "text": "utors that facilitated the collection of a large multitask mixture with multiple prompts per dataset" }, { "category_id": 15, "poly": [ 295.0, 1658.0, 1404.0, 1658.0, 1404.0, 1690.0, 295.0, 1690.0 ], "score": 0.983, "text": "(Bach et al.,2O22).We then train a variant of the T5 encoder-decoder model (Rafel et al., 2020;" }, { "category_id": 15, "poly": [ 295.0, 1690.0, 1404.0, 1690.0, 1404.0, 1722.0, 295.0, 1722.0 ], "score": 0.988, "text": "Lester et al.,2O21) on a subset of the tasks (each with multiple datasets)and then evaluate tasks and" }, { "category_id": 15, "poly": [ 292.0, 1720.0, 778.0, 1720.0, 778.0, 1753.0, 292.0, 1753.0 ], "score": 0.98, "text": "prompts that the model was not trained on." }, { "category_id": 15, "poly": [ 292.0, 1212.0, 1407.0, 1212.0, 1407.0, 1253.0, 292.0, 1253.0 ], "score": 0.985, "text": " Further, it is an open question how implicit this multitask learning really is. Given the scale of recent" }, { "category_id": 15, "poly": [ 295.0, 1248.0, 1405.0, 1248.0, 1405.0, 1282.0, 295.0, 1282.0 ], "score": 0.99, "text": "language models' pretraining corpora,it is reasonable to expect that some common natural language" }, { "category_id": 15, "poly": [ 292.0, 1276.0, 1404.0, 1276.0, 1404.0, 1312.0, 292.0, 1312.0 ], "score": 0.987, "text": "processing (NLP) tasks would appear in an explicit form in their pretraining corpora,thereby directly" }, { "category_id": 15, "poly": [ 296.0, 1310.0, 1404.0, 1310.0, 1404.0, 1341.0, 296.0, 1341.0 ], "score": 0.996, "text": "training the models on those tasks.For example,there are many websites that simply contain lists" }, { "category_id": 15, "poly": [ 293.0, 1338.0, 1403.0, 1338.0, 1403.0, 1373.0, 293.0, 1373.0 ], "score": 0.989, "text": "of trivia questions and answers,1 which are precisely supervised training data for the task of closed-" }, { "category_id": 15, "poly": [ 292.0, 1365.0, 1407.0, 1365.0, 1407.0, 1406.0, 292.0, 1406.0 ], "score": 0.979, "text": "book question answering (Roberts et al., 2020). We hypothesize that such multitask supervision in" }, { "category_id": 15, "poly": [ 293.0, 1400.0, 930.0, 1400.0, 930.0, 1433.0, 293.0, 1433.0 ], "score": 0.993, "text": "pretraining plays a large role in zero-shot generalization." }, { "category_id": 15, "poly": [ 294.0, 1765.0, 1405.0, 1765.0, 1405.0, 1801.0, 294.0, 1801.0 ], "score": 0.991, "text": "Our experiments study two questions. First, does multitask prompted training improve generaliza-" }, { "category_id": 15, "poly": [ 293.0, 1796.0, 1407.0, 1796.0, 1407.0, 1833.0, 293.0, 1833.0 ], "score": 0.98, "text": "tion to unseen tasks? Second, does training on a wider range of prompts improve robustness to" }, { "category_id": 15, "poly": [ 291.0, 1826.0, 1407.0, 1826.0, 1407.0, 1863.0, 291.0, 1863.0 ], "score": 0.984, "text": " prompt wording?For the first question, we find that multitask training enables zero-shot task gen-" }, { "category_id": 15, "poly": [ 293.0, 1858.0, 1404.0, 1858.0, 1404.0, 1890.0, 293.0, 1890.0 ], "score": 0.987, "text": "eralization by showing that our model matches or exceeds the performance of GPT-3 (Brown et al.," }, { "category_id": 15, "poly": [ 293.0, 1887.0, 939.0, 1887.0, 939.0, 1923.0, 293.0, 1923.0 ], "score": 0.995, "text": "2020)on 9 out of 11 held-out datasets,despite being about" }, { "category_id": 15, "poly": [ 992.0, 1887.0, 1405.0, 1887.0, 1405.0, 1923.0, 992.0, 1923.0 ], "score": 0.983, "text": "smaller. We also show that the model" }, { "category_id": 15, "poly": [ 293.0, 1919.0, 1406.0, 1919.0, 1406.0, 1951.0, 293.0, 1951.0 ], "score": 0.989, "text": "improves over a large baseline language model on 13 out of 14 tasks in the BIG-bench benchmark" } ], "page_info": { "page_no": 1, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 465, 1403, 465, 1403, 681, 298, 681 ], "score": 0.98 }, { "category_id": 1, "poly": [ 298, 955, 1403, 955, 1403, 1230, 298, 1230 ], "score": 0.98 }, { "category_id": 1, "poly": [ 298, 695, 1403, 695, 1403, 941, 298, 941 ], "score": 0.979 }, { "category_id": 1, "poly": [ 299, 1850, 1402, 1850, 1402, 2034, 299, 2034 ], "score": 0.978 }, { "category_id": 1, "poly": [ 299, 1680, 1402, 1680, 1402, 1835, 299, 1835 ], "score": 0.977 }, { "category_id": 1, "poly": [ 299, 229, 1402, 229, 1402, 353, 299, 353 ], "score": 0.976 }, { "category_id": 1, "poly": [ 298, 1246, 1404, 1246, 1404, 1461, 298, 1461 ], "score": 0.97 }, { "category_id": 1, "poly": [ 302, 1572, 1402, 1572, 1402, 1665, 302, 1665 ], "score": 0.935 }, { "category_id": 0, "poly": [ 300, 396, 587, 396, 587, 433, 300, 433 ], "score": 0.908 }, { "category_id": 0, "poly": [ 300, 1504, 1071, 1504, 1071, 1539, 300, 1539 ], "score": 0.904 }, { "category_id": 2, "poly": [ 300, 76, 815, 76, 815, 104, 300, 104 ], "score": 0.895 }, { "category_id": 2, "poly": [ 841, 2088, 858, 2088, 858, 2112, 841, 2112 ], "score": 0.601 }, { "category_id": 2, "poly": [ 842, 2088, 859, 2088, 859, 2111, 842, 2111 ], "score": 0.378 }, { "category_id": 15, "poly": [ 292.0, 394.0, 593.0, 394.0, 593.0, 438.0, 292.0, 438.0 ], "score": 0.99, "text": "2RELATED WORK" }, { "category_id": 15, "poly": [ 291.0, 1500.0, 1076.0, 1500.0, 1076.0, 1546.0, 291.0, 1546.0 ], "score": 0.997, "text": "3MEASURING GENERALIZATION TO UNSEEN TASKS" }, { "category_id": 15, "poly": [ 296.0, 73.0, 817.0, 73.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 839.0, 2085.0, 860.0, 2085.0, 860.0, 2116.0, 839.0, 2116.0 ], "score": 1.0, "text": "3" }, { "category_id": 15, "poly": [ 839.0, 2086.0, 860.0, 2086.0, 860.0, 2117.0, 839.0, 2117.0 ], "score": 1.0, "text": "3" }, { "category_id": 15, "poly": [ 292.0, 463.0, 1407.0, 463.0, 1407.0, 503.0, 292.0, 503.0 ], "score": 0.99, "text": " In this work, we distinguish implicit multitask learning in language model pretraining from explicit" }, { "category_id": 15, "poly": [ 293.0, 494.0, 1405.0, 494.0, 1405.0, 532.0, 293.0, 532.0 ], "score": 0.99, "text": "multitask learning (Caruana,1997),the technique for mixing multiple tasks into a single supervised" }, { "category_id": 15, "poly": [ 293.0, 526.0, 1405.0, 526.0, 1405.0, 563.0, 293.0, 563.0 ], "score": 0.989, "text": "training process.Models trained with multitask learning have long been shown to have improved" }, { "category_id": 15, "poly": [ 293.0, 557.0, 1403.0, 557.0, 1403.0, 595.0, 293.0, 595.0 ], "score": 0.982, "text": "performance in NLP (Collobert and Weston,2O08). Since different tasks have different outputs,ap-" }, { "category_id": 15, "poly": [ 293.0, 588.0, 1405.0, 588.0, 1405.0, 622.0, 293.0, 622.0 ], "score": 0.987, "text": " plying multitask learning requires a shared format, and various have ben used (Hashimoto et al.," }, { "category_id": 15, "poly": [ 297.0, 620.0, 1405.0, 620.0, 1405.0, 651.0, 297.0, 651.0 ], "score": 0.986, "text": "2016; McCann et al.,2O18). Several multitask works also explore few-shot and zero-shot general-" }, { "category_id": 15, "poly": [ 294.0, 650.0, 1301.0, 650.0, 1301.0, 684.0, 294.0, 684.0 ], "score": 0.974, "text": "ization to new datasets with large pretrained models (e.g., Vu et al.,2O2O; Ye et al., 2021)." }, { "category_id": 15, "poly": [ 294.0, 956.0, 1405.0, 956.0, 1405.0, 992.0, 294.0, 992.0 ], "score": 0.977, "text": "More generally, Schick and Schutze (2O21) and Brown et al. (2O2O) popularized using prompts as" }, { "category_id": 15, "poly": [ 292.0, 987.0, 1407.0, 987.0, 1407.0, 1021.0, 292.0, 1021.0 ], "score": 0.986, "text": "a generic method for all NLP tasks.Mishra et al.(2O21) further extend this approach to a multitask" }, { "category_id": 15, "poly": [ 291.0, 1014.0, 1408.0, 1014.0, 1408.0, 1056.0, 291.0, 1056.0 ], "score": 0.987, "text": " setup, training on prompts for 61 narrowly defined tasks (e.g., question generation, incorrect answer" }, { "category_id": 15, "poly": [ 293.0, 1047.0, 1405.0, 1047.0, 1405.0, 1081.0, 293.0, 1081.0 ], "score": 0.989, "text": " generation) adapted from 9 datasets’ crowdsourcing instructions, whereas we train on and measure" }, { "category_id": 15, "poly": [ 293.0, 1078.0, 1404.0, 1078.0, 1404.0, 1111.0, 293.0, 1111.0 ], "score": 0.983, "text": "generalization across 62 datasets and 12 tasks as traditionally defined in the NLP literature (83)." }, { "category_id": 15, "poly": [ 296.0, 1109.0, 1404.0, 1109.0, 1404.0, 1142.0, 296.0, 1142.0 ], "score": 0.995, "text": "Additionally,their prompts include labeled examples in addition to instructions,whereas we focus" }, { "category_id": 15, "poly": [ 296.0, 1139.0, 1404.0, 1139.0, 1404.0, 1172.0, 296.0, 1172.0 ], "score": 0.98, "text": "on zero-shot generalization. Lastly,concurrent work by Wei et al. (2O21) shares a similar research" }, { "category_id": 15, "poly": [ 292.0, 1168.0, 1406.0, 1168.0, 1406.0, 1206.0, 292.0, 1206.0 ], "score": 0.983, "text": "question with us, although we differ in several substantive regards,e.g., prompt diversity, model" }, { "category_id": 15, "poly": [ 294.0, 1199.0, 1209.0, 1199.0, 1209.0, 1232.0, 294.0, 1232.0 ], "score": 0.994, "text": "scale,and held-out-task scheme.We discuss our differences in detail in Section 7." }, { "category_id": 15, "poly": [ 292.0, 693.0, 1407.0, 693.0, 1407.0, 732.0, 292.0, 732.0 ], "score": 0.986, "text": "Natural language prompting is the method of reformating NLP tasks in the format of a natural" }, { "category_id": 15, "poly": [ 293.0, 727.0, 1405.0, 727.0, 1405.0, 760.0, 293.0, 760.0 ], "score": 0.994, "text": "language response to natural language input. The development of text-to-text pretrained models" }, { "category_id": 15, "poly": [ 293.0, 753.0, 1403.0, 753.0, 1403.0, 793.0, 293.0, 793.0 ], "score": 0.974, "text": " such as T5 (Raffel et al., 2O2O) makes prompts a particularly useful method for multitask learning." }, { "category_id": 15, "poly": [ 294.0, 788.0, 1405.0, 788.0, 1405.0, 822.0, 294.0, 822.0 ], "score": 0.967, "text": "For example, Khashabi et al. (2O2Oa) reformat 2O question-answering datasets into a single prompt" }, { "category_id": 15, "poly": [ 294.0, 818.0, 1405.0, 818.0, 1405.0, 852.0, 294.0, 852.0 ], "score": 0.937, "text": "of question: ... (A)... (B)... (C)... context: ...,while later work such as" }, { "category_id": 15, "poly": [ 292.0, 846.0, 1405.0, 846.0, 1405.0, 885.0, 292.0, 885.0 ], "score": 0.986, "text": " Zhong et al. (2O21) and Wang et al. (2O21) cast a range of datasets into a single boolean QA prompt" }, { "category_id": 15, "poly": [ 293.0, 878.0, 1407.0, 878.0, 1407.0, 915.0, 293.0, 915.0 ], "score": 0.988, "text": "or a single NLI prompt, respectively. Although effective, these single-prompt methods typically do" }, { "category_id": 15, "poly": [ 293.0, 911.0, 1171.0, 911.0, 1171.0, 944.0, 293.0, 944.0 ], "score": 0.984, "text": "not generalize to new prompts or new tasks inexpressible in their fixed format." }, { "category_id": 15, "poly": [ 294.0, 1848.0, 1407.0, 1848.0, 1407.0, 1887.0, 294.0, 1887.0 ], "score": 0.988, "text": "Noting that grouping by task is an imperfect heuristic, we err on the side of organizing our task" }, { "category_id": 15, "poly": [ 294.0, 1881.0, 1406.0, 1881.0, 1406.0, 1917.0, 294.0, 1917.0 ], "score": 0.997, "text": "taxonomy according to the task format as opposed to required skill based on conventions in the" }, { "category_id": 15, "poly": [ 294.0, 1909.0, 1406.0, 1909.0, 1406.0, 1946.0, 294.0, 1946.0 ], "score": 0.972, "text": "literature (Khashabi et al.,202Ob; Vu et al.,202O; Ye et al.,2021).We collect all datasets from" }, { "category_id": 15, "poly": [ 294.0, 1941.0, 1406.0, 1941.0, 1406.0, 1979.0, 294.0, 1979.0 ], "score": 0.982, "text": " these papers and exclude those that are not in English (which also excludes programming languages" }, { "category_id": 15, "poly": [ 293.0, 1967.0, 1407.0, 1967.0, 1407.0, 2012.0, 293.0, 2012.0 ], "score": 0.989, "text": " and structured annotations such as parse trees) or if they require special domain knowledge (e.g.," }, { "category_id": 15, "poly": [ 294.0, 2000.0, 1406.0, 2000.0, 1406.0, 2040.0, 294.0, 2040.0 ], "score": 0.988, "text": "biomedicine). This yields 12 tasks and 62 datasets with publicly contributed prompts in our training" }, { "category_id": 15, "poly": [ 297.0, 1682.0, 1406.0, 1682.0, 1406.0, 1715.0, 297.0, 1715.0 ], "score": 0.983, "text": "Unfortunately,NLP task categorization is fuzzy,particularly if one tries to isolate aunique skill.For" }, { "category_id": 15, "poly": [ 295.0, 1712.0, 1403.0, 1712.0, 1403.0, 1746.0, 295.0, 1746.0 ], "score": 0.989, "text": " example, many datasets evaluate commonsense knowledge,and some multitask works (e.g., Brown" }, { "category_id": 15, "poly": [ 294.0, 1742.0, 1404.0, 1742.0, 1404.0, 1775.0, 294.0, 1775.0 ], "score": 0.965, "text": "et al.,202O; Wei et al.,2O21) define commonsense as a standalone task. However, commonsense" }, { "category_id": 15, "poly": [ 293.0, 1771.0, 1407.0, 1771.0, 1407.0, 1811.0, 293.0, 1811.0 ], "score": 0.984, "text": " datasets differ vastly, ranging from innate knowledge and grade-school science to DIY instructions," }, { "category_id": 15, "poly": [ 295.0, 1801.0, 1334.0, 1801.0, 1334.0, 1839.0, 295.0, 1839.0 ], "score": 0.989, "text": "US cultural norms,and graduate-level theorems (see Appendix E.1 for a detailed discussion)." }, { "category_id": 15, "poly": [ 293.0, 227.0, 1406.0, 227.0, 1406.0, 268.0, 293.0, 268.0 ], "score": 0.984, "text": "(BIG-bench collaboration, 2021). For the second question, we find that training on more prompts per" }, { "category_id": 15, "poly": [ 295.0, 261.0, 1404.0, 261.0, 1404.0, 293.0, 295.0, 293.0 ], "score": 0.992, "text": "dataset consistently improves the median and decreases the variability of performance on held-out" }, { "category_id": 15, "poly": [ 294.0, 292.0, 1407.0, 292.0, 1407.0, 328.0, 294.0, 328.0 ], "score": 0.992, "text": " tasks. Training on prompts from a wider range of datasets also generally improves the median but" }, { "category_id": 15, "poly": [ 294.0, 321.0, 803.0, 321.0, 803.0, 356.0, 294.0, 356.0 ], "score": 0.991, "text": "does not consistently decrease the variability." }, { "category_id": 15, "poly": [ 294.0, 1246.0, 1404.0, 1246.0, 1404.0, 1281.0, 294.0, 1281.0 ], "score": 0.988, "text": "Finally, in explaining the success of prompts,the leading hypothesis is that models learn to under-" }, { "category_id": 15, "poly": [ 294.0, 1277.0, 1405.0, 1277.0, 1405.0, 1312.0, 294.0, 1312.0 ], "score": 0.987, "text": " stand the prompts as task instructions which help them generalize to unseen tasks (Wei et al., 2021;" }, { "category_id": 15, "poly": [ 294.0, 1307.0, 1405.0, 1307.0, 1405.0, 1341.0, 294.0, 1341.0 ], "score": 0.961, "text": "Mishra et al.,2021; Schick and Schuitze,2021; Brown et al., 202O).However,the extent to which" }, { "category_id": 15, "poly": [ 296.0, 1339.0, 1405.0, 1339.0, 1405.0, 1373.0, 296.0, 1373.0 ], "score": 0.983, "text": "this success depends on the semantic meaningfulness of the prompts has been challenged (Webson" }, { "category_id": 15, "poly": [ 293.0, 1367.0, 1404.0, 1367.0, 1404.0, 1404.0, 293.0, 1404.0 ], "score": 0.977, "text": "and Pavlick,2021; Logan et al.,2021). Thus,in this work,we remain agnostic as to why prompts" }, { "category_id": 15, "poly": [ 293.0, 1399.0, 1406.0, 1399.0, 1406.0, 1437.0, 293.0, 1437.0 ], "score": 0.981, "text": " support generalization. We only claim that prompts serve as a natural format for multitask training" }, { "category_id": 15, "poly": [ 294.0, 1430.0, 951.0, 1430.0, 951.0, 1464.0, 294.0, 1464.0 ], "score": 0.987, "text": "which empirically supports generalization to unseen tasks." }, { "category_id": 15, "poly": [ 294.0, 1571.0, 1406.0, 1571.0, 1406.0, 1606.0, 294.0, 1606.0 ], "score": 0.984, "text": "We begin by assuming an underlying partition of NLP datasets into tasks.We use the term “task”" }, { "category_id": 15, "poly": [ 296.0, 1605.0, 1405.0, 1605.0, 1405.0, 1639.0, 296.0, 1639.0 ], "score": 0.979, "text": "to refer to a general NLP ability that is tested by a group of specific datasets.To evaluate zero-shot" }, { "category_id": 15, "poly": [ 296.0, 1635.0, 1399.0, 1635.0, 1399.0, 1668.0, 296.0, 1668.0 ], "score": 0.978, "text": " generalization to new tasks, we train on a subset of tasks and evaluate on a held-out group of tasks." } ], "page_info": { "page_no": 2, "width": 1700, "height": 2200 } }, 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See" }, { "category_id": 15, "poly": [ 294.0, 839.0, 1406.0, 839.0, 1406.0, 880.0, 294.0, 880.0 ], "score": 0.983, "text": "Table 5 for the full list.) Color represents the level of supervision. Yellow datasets are in the training" }, { "category_id": 15, "poly": [ 293.0, 873.0, 1405.0, 873.0, 1405.0, 908.0, 293.0, 908.0 ], "score": 0.983, "text": " mixture. Green datasets are held out and represent tasks that were not seen during training. Hotpot" }, { "category_id": 15, "poly": [ 296.0, 905.0, 935.0, 905.0, 935.0, 937.0, 296.0, 937.0 ], "score": 0.983, "text": "QA is recast as closed-book QA due to long input length." }, { "category_id": 15, "poly": [ 295.0, 1598.0, 766.0, 1598.0, 766.0, 1635.0, 295.0, 1635.0 ], "score": 0.979, "text": "4AUNIFIED PROMPTFORMAT" }, { "category_id": 15, "poly": [ 838.0, 2086.0, 862.0, 2086.0, 862.0, 2118.0, 838.0, 2118.0 ], "score": 1.0, "text": "4" }, { "category_id": 15, "poly": [ 296.0, 73.0, 817.0, 73.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 296.0, 73.0, 817.0, 73.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.992, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 1665.0, 1404.0, 1665.0, 1404.0, 1705.0, 293.0, 1705.0 ], "score": 0.99, "text": "All datasets are given to our model in natural language prompted form to enable zero-shot exper-" }, { "category_id": 15, "poly": [ 293.0, 1698.0, 1404.0, 1698.0, 1404.0, 1735.0, 293.0, 1735.0 ], "score": 0.987, "text": " imentation. To facilitate writing a large collection of prompts, we develop a templating language" }, { "category_id": 15, "poly": [ 293.0, 1727.0, 1405.0, 1727.0, 1405.0, 1766.0, 293.0, 1766.0 ], "score": 0.985, "text": " and an application that make it easy to convert diverse datasets into prompts. We define a prompt" }, { "category_id": 15, "poly": [ 292.0, 1761.0, 1402.0, 1761.0, 1402.0, 1793.0, 292.0, 1793.0 ], "score": 0.979, "text": " as consisting of an input template and a target template,along with a collection of associated meta-" }, { "category_id": 15, "poly": [ 293.0, 1789.0, 1405.0, 1789.0, 1405.0, 1827.0, 293.0, 1827.0 ], "score": 0.996, "text": "data. The templates are functions mapping a data example into natural language for the input and" }, { "category_id": 15, "poly": [ 294.0, 1821.0, 1405.0, 1821.0, 1405.0, 1855.0, 294.0, 1855.0 ], "score": 0.987, "text": "target sequences.Practically,the templates alow the user to mix arbitrary text with the data fields," }, { "category_id": 15, "poly": [ 293.0, 1851.0, 1406.0, 1851.0, 1406.0, 1884.0, 293.0, 1884.0 ], "score": 0.991, "text": "metadata,and other code for rendering and formatting raw fields.For example,in the case of an NLI" }, { "category_id": 15, "poly": [ 293.0, 1880.0, 1404.0, 1880.0, 1404.0, 1915.0, 293.0, 1915.0 ], "score": 0.982, "text": "dataset,the example would include fields for Premise， Hypot hesis， Label.An input tem-" }, { "category_id": 15, "poly": [ 293.0, 1910.0, 472.0, 1910.0, 472.0, 1948.0, 293.0, 1948.0 ], "score": 0.968, "text": " plate would be" }, { "category_id": 15, "poly": [ 671.0, 1910.0, 1181.0, 1910.0, 1181.0, 1948.0, 671.0, 1948.0 ], "score": 0.976, "text": " is true，is it also true that" }, { "category_id": 15, "poly": [ 1389.0, 1910.0, 1404.0, 1910.0, 1404.0, 1948.0, 1389.0, 1948.0 ], "score": 0.745, "text": "，，" }, { "category_id": 15, "poly": [ 296.0, 1943.0, 1063.0, 1943.0, 1063.0, 1977.0, 296.0, 1977.0 ], "score": 0.989, "text": "whereas a target template can be defined with the label choices" }, { "category_id": 15, "poly": [ 1327.0, 1943.0, 1404.0, 1943.0, 1404.0, 1977.0, 1327.0, 1977.0 ], "score": 0.991, "text": ".Here" }, { "category_id": 15, "poly": [ 294.0, 1974.0, 1404.0, 1974.0, 1404.0, 2008.0, 294.0, 2008.0 ], "score": 0.987, "text": "Choices is prompt-specific metadata that consists of the options yes， maybe， no correspond-" }, { "category_id": 15, "poly": [ 294.0, 2004.0, 1404.0, 2004.0, 1404.0, 2035.0, 294.0, 2035.0 ], "score": 0.978, "text": "ing to label being entailment (O), neutral (1) or contradiction (2). 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Alltasks from BIG-bench are novel tasks that were unseen in our training." }, { "category_id": 15, "poly": [ 295.0, 966.0, 1406.0, 966.0, 1406.0, 1006.0, 295.0, 1006.0 ], "score": 0.982, "text": "and evaluation mixtures (Figure 2) as of writing. All experiments use datasets in the Hugging Face" }, { "category_id": 15, "poly": [ 295.0, 998.0, 710.0, 998.0, 710.0, 1034.0, 295.0, 1034.0 ], "score": 0.984, "text": "datasets library (Lhoest et al., 2021)." }, { "category_id": 15, "poly": [ 293.0, 1041.0, 1405.0, 1041.0, 1405.0, 1084.0, 293.0, 1084.0 ], "score": 0.991, "text": "To test zero-shot generalization, we hold out all constituent datasets of four tasks: natural language" }, { "category_id": 15, "poly": [ 293.0, 1075.0, 1406.0, 1075.0, 1406.0, 1112.0, 293.0, 1112.0 ], "score": 0.983, "text": " inference (NLI), coreference resolution, sentence completion,and word sense disambiguation. We" }, { "category_id": 15, "poly": [ 293.0, 1105.0, 1404.0, 1105.0, 1404.0, 1142.0, 293.0, 1142.0 ], "score": 0.991, "text": "choose NLI as a held-out task because humans also zero-shot generalize to NLI as an unseen task:" }, { "category_id": 15, "poly": [ 293.0, 1138.0, 1404.0, 1138.0, 1404.0, 1173.0, 293.0, 1173.0 ], "score": 0.987, "text": "Most humans are never explicitly trained to classify whether a premise sentence entails or contradicts" }, { "category_id": 15, "poly": [ 293.0, 1168.0, 1404.0, 1168.0, 1404.0, 1204.0, 293.0, 1204.0 ], "score": 0.99, "text": "a hypothesis sentence, yet they find it intuitive to perform this task without training (Williams et al.," }, { "category_id": 15, "poly": [ 293.0, 1195.0, 1404.0, 1195.0, 1404.0, 1233.0, 293.0, 1233.0 ], "score": 0.992, "text": "2020).For the same reason, we also hold out coreference resolution and word sense disambiguation." }, { "category_id": 15, "poly": [ 293.0, 1227.0, 1404.0, 1227.0, 1404.0, 1264.0, 293.0, 1264.0 ], "score": 0.984, "text": "We further hold out sentence completion because it is a task possibly too similar to NLI (Appendix" }, { "category_id": 15, "poly": [ 292.0, 1256.0, 1406.0, 1256.0, 1406.0, 1296.0, 292.0, 1296.0 ], "score": 0.975, "text": "E.2 discusses this in detail). Additionally, we do not train our main model on any datasets that Brown" }, { "category_id": 15, "poly": [ 292.0, 1290.0, 1406.0, 1290.0, 1406.0, 1324.0, 292.0, 1324.0 ], "score": 0.982, "text": "et al. (2O2O) used for evaluation,so that our main results will be a fair zero-shot comparison. We" }, { "category_id": 15, "poly": [ 295.0, 1319.0, 1341.0, 1319.0, 1341.0, 1356.0, 295.0, 1356.0 ], "score": 0.984, "text": "also verify that data for those tasks is not leaked through the pretraining corpus (Appendix F)." } ], "page_info": { "page_no": 3, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 788, 1404, 788, 1404, 1154, 298, 1154 ], "score": 0.982 }, { "category_id": 1, "poly": [ 298, 1339, 1404, 1339, 1404, 1553, 298, 1553 ], "score": 0.98 }, { "category_id": 1, "poly": [ 298, 1850, 1403, 1850, 1403, 2034, 298, 2034 ], "score": 0.978 }, { "category_id": 1, "poly": [ 298, 1681, 1402, 1681, 1402, 1835, 298, 1835 ], "score": 0.977 }, { "category_id": 1, "poly": [ 298, 1170, 1403, 1170, 1403, 1323, 298, 1323 ], "score": 0.976 }, { "category_id": 3, "poly": [ 359, 129, 1347, 129, 1347, 453, 359, 453 ], "score": 0.974 }, { "category_id": 4, "poly": [ 296, 486, 1405, 486, 1405, 670, 296, 670 ], "score": 0.962 }, { "category_id": 1, 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controller?" }, { "category_id": 15, "poly": [ 916.0, 240.0, 977.0, 240.0, 977.0, 265.0, 916.0, 265.0 ], "score": 0.999, "text": "Summary" }, { "category_id": 15, "poly": [ 999.0, 242.0, 1292.0, 242.0, 1292.0, 260.0, 999.0, 260.0 ], "score": 0.986, "text": "Graffiti artist Banksy is believed to be" }, { "category_id": 15, "poly": [ 392.0, 256.0, 438.0, 256.0, 438.0, 279.0, 392.0, 279.0 ], "score": 0.983, "text": "Label" }, { "category_id": 15, "poly": [ 477.0, 257.0, 495.0, 257.0, 495.0, 277.0, 477.0, 277.0 ], "score": 0.729, "text": "0" }, { "category_id": 15, "poly": [ 999.0, 255.0, 1074.0, 255.0, 1074.0, 279.0, 999.0, 279.0 ], "score": 0.965, "text": "behind..." }, { "category_id": 15, "poly": [ 388.0, 308.0, 554.0, 308.0, 554.0, 325.0, 388.0, 325.0 ], "score": 0.983, "text": "{Question1} {Question2}" }, { "category_id": 15, "poly": [ 617.0, 304.0, 801.0, 304.0, 801.0, 325.0, 617.0, 325.0 ], "score": 0.982, "text": "I received the questions" }, { "category_id": 15, "poly": [ 935.0, 304.0, 1013.0, 304.0, 1013.0, 325.0, 935.0, 325.0 ], "score": 0.992, "text": "{Document}" }, { "category_id": 15, "poly": [ 1094.0, 306.0, 1322.0, 306.0, 1322.0, 324.0, 1094.0, 324.0 ], "score": 0.968, "text": "First,please read the article:" }, { "category_id": 15, "poly": [ 376.0, 322.0, 564.0, 322.0, 564.0, 344.0, 376.0, 344.0 ], "score": 0.984, "text": "Pick one:These questions" }, { "category_id": 15, "poly": [ 647.0, 321.0, 775.0, 321.0, 775.0, 342.0, 647.0, 342.0 ], "score": 0.963, "text": "\"{Question1}\" and" }, { "category_id": 15, "poly": [ 921.0, 322.0, 1025.0, 322.0, 1025.0, 343.0, 921.0, 343.0 ], "score": 0.972, "text": "How would you" }, { "category_id": 15, "poly": [ 1170.0, 322.0, 1249.0, 322.0, 1249.0, 343.0, 1170.0, 343.0 ], "score": 0.993, "text": "{Document}" }, { "category_id": 15, "poly": [ 392.0, 341.0, 550.0, 341.0, 550.0, 358.0, 392.0, 358.0 ], "score": 0.964, "text": "are duplicates or not" }, { "category_id": 15, "poly": [ 624.0, 336.0, 795.0, 336.0, 795.0, 360.0, 624.0, 360.0 ], "score": 0.966, "text": "\"{Question2}\".Are they" }, { "category_id": 15, "poly": [ 912.0, 338.0, 1035.0, 338.0, 1035.0, 359.0, 912.0, 359.0 ], "score": 0.971, "text": "rephrase that in" }, { "category_id": 15, "poly": [ 1116.0, 335.0, 1302.0, 335.0, 1302.0, 361.0, 1116.0, 361.0 ], "score": 0.919, "text": "Now,can you write me an" }, { "category_id": 15, "poly": [ 427.0, 356.0, 511.0, 356.0, 511.0, 377.0, 427.0, 377.0 ], "score": 0.998, "text": "duplicates." }, { "category_id": 15, "poly": [ 667.0, 355.0, 754.0, 355.0, 754.0, 376.0, 667.0, 376.0 ], "score": 0.992, "text": "duplicates?" }, { "category_id": 15, "poly": [ 924.0, 355.0, 1021.0, 355.0, 1021.0, 376.0, 924.0, 376.0 ], "score": 0.93, "text": "a few words?" }, { "category_id": 15, "poly": [ 1090.0, 354.0, 1329.0, 354.0, 1329.0, 375.0, 1090.0, 375.0 ], "score": 0.982, "text": "extremely short abstract for it?" }, { "category_id": 15, "poly": [ 410.0, 416.0, 532.0, 416.0, 532.0, 441.0, 410.0, 441.0 ], "score": 0.963, "text": "{Choices[label]}" }, { "category_id": 15, "poly": [ 649.0, 416.0, 770.0, 416.0, 770.0, 440.0, 649.0, 440.0 ], "score": 0.979, "text": "{Choices[label]}" }, { "category_id": 15, "poly": [ 938.0, 417.0, 1009.0, 417.0, 1009.0, 439.0, 938.0, 439.0 ], "score": 0.987, "text": "{Summary}" }, { "category_id": 15, "poly": [ 1172.0, 413.0, 1247.0, 413.0, 1247.0, 443.0, 1172.0, 443.0 ], "score": 0.896, "text": "{Summary}" }, { "category_id": 15, "poly": [ 294.0, 487.0, 1403.0, 487.0, 1403.0, 522.0, 294.0, 522.0 ], "score": 0.99, "text": "Figure 3: Prompt templates from the P3 prompt collection. Each dataset has multiple prompt tem-" }, { "category_id": 15, "poly": [ 294.0, 517.0, 1405.0, 517.0, 1405.0, 553.0, 294.0, 553.0 ], "score": 0.99, "text": "plates consisting of an input and a target template. These use the fields of the raw data examples as" }, { "category_id": 15, "poly": [ 295.0, 548.0, 1402.0, 548.0, 1402.0, 583.0, 295.0, 583.0 ], "score": 0.99, "text": "wellas template metadata, e.g., the left paraphrasing identification prompts use Choices,a template-" }, { "category_id": 15, "poly": [ 294.0, 578.0, 1405.0, 578.0, 1405.0, 614.0, 294.0, 614.0 ], "score": 0.986, "text": "level list variable['Not duplicates'，'Duplicates'].These templates are materialized" }, { "category_id": 15, "poly": [ 295.0, 611.0, 1403.0, 611.0, 1403.0, 643.0, 295.0, 643.0 ], "score": 0.982, "text": "to produce the prompted instance shown in Figure 1.The complete set of prompt templates used in" }, { "category_id": 15, "poly": [ 295.0, 639.0, 606.0, 639.0, 606.0, 676.0, 295.0, 676.0 ], "score": 0.984, "text": "TO is given in Appendix H." }, { "category_id": 15, "poly": [ 294.0, 1606.0, 681.0, 1606.0, 681.0, 1646.0, 294.0, 1646.0 ], "score": 0.999, "text": "5EXPERIMENTAL SETUP" }, { "category_id": 15, "poly": [ 294.0, 72.0, 818.0, 72.0, 818.0, 110.0, 294.0, 110.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 839.0, 2085.0, 861.0, 2085.0, 861.0, 2120.0, 839.0, 2120.0 ], "score": 1.0, "text": "5" }, { "category_id": 15, "poly": [ 293.0, 785.0, 1406.0, 785.0, 1406.0, 825.0, 293.0, 825.0 ], "score": 0.995, "text": "To develop prompts, we built an interface for interactively writing prompts on datasets.We put out an" }, { "category_id": 15, "poly": [ 293.0, 818.0, 1405.0, 818.0, 1405.0, 854.0, 293.0, 854.0 ], "score": 0.991, "text": "open callin the research community for users to contribute prompts.36 contributors afliated with" }, { "category_id": 15, "poly": [ 293.0, 848.0, 1404.0, 848.0, 1404.0, 885.0, 293.0, 885.0 ], "score": 0.994, "text": "24 institutions in 8 countries participated. Since our goal was to train a model to be robust to prompt" }, { "category_id": 15, "poly": [ 294.0, 881.0, 1405.0, 881.0, 1405.0, 915.0, 294.0, 915.0 ], "score": 0.984, "text": "format, and since the question of what makes a prompt effective remains unresolved (Webson and" }, { "category_id": 15, "poly": [ 293.0, 909.0, 1405.0, 909.0, 1405.0, 944.0, 293.0, 944.0 ], "score": 0.979, "text": "Pavlick,2021; Logan et al.,2021; Zhao et al.,2O21),we encouraged contributors to be open in their" }, { "category_id": 15, "poly": [ 294.0, 942.0, 1405.0, 942.0, 1405.0, 976.0, 294.0, 976.0 ], "score": 0.991, "text": " style and create a diverse set of prompts.The main annotation guideline was that prompts needed" }, { "category_id": 15, "poly": [ 294.0, 972.0, 1404.0, 972.0, 1404.0, 1006.0, 294.0, 1006.0 ], "score": 0.982, "text": "to be grammatical and understandable by a fluent English speaker with no prior experience of the" }, { "category_id": 15, "poly": [ 292.0, 999.0, 1404.0, 999.0, 1404.0, 1038.0, 292.0, 1038.0 ], "score": 0.983, "text": " tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in" }, { "category_id": 15, "poly": [ 292.0, 1029.0, 1405.0, 1029.0, 1405.0, 1071.0, 292.0, 1071.0 ], "score": 0.989, "text": "favor of natural language variants. For example, instead of predicting indices of a span extracting" }, { "category_id": 15, "poly": [ 293.0, 1062.0, 1406.0, 1062.0, 1406.0, 1100.0, 293.0, 1100.0 ], "score": 0.986, "text": " answers from a passage,the model is expected to copy the span's text instead. With these minimal" }, { "category_id": 15, "poly": [ 293.0, 1094.0, 1405.0, 1094.0, 1405.0, 1131.0, 293.0, 1131.0 ], "score": 0.993, "text": "constraints, prompt writers were encouraged to use both formal and creative prompts and various" }, { "category_id": 15, "poly": [ 293.0, 1124.0, 539.0, 1124.0, 539.0, 1158.0, 293.0, 1158.0 ], "score": 0.994, "text": "orderings of the data." }, { "category_id": 15, "poly": [ 294.0, 1338.0, 1405.0, 1338.0, 1405.0, 1376.0, 294.0, 1376.0 ], "score": 0.989, "text": "The details of the prompting language and tool are given in Appendix D and Bach et al. (2022)," }, { "category_id": 15, "poly": [ 294.0, 1369.0, 1405.0, 1369.0, 1405.0, 1407.0, 294.0, 1407.0 ], "score": 0.985, "text": "and the prompts themselves are given in Appendix H. We collected prompts for English datasets," }, { "category_id": 15, "poly": [ 292.0, 1398.0, 1404.0, 1398.0, 1404.0, 1437.0, 292.0, 1437.0 ], "score": 0.993, "text": " excluding ones that included potentially harmful content or non-natural language such as program-" }, { "category_id": 15, "poly": [ 292.0, 1430.0, 1405.0, 1430.0, 1405.0, 1466.0, 292.0, 1466.0 ], "score": 0.985, "text": "ming languages. We refer to this collection as the Public Pool of Prompts (P3).As of writing, P3" }, { "category_id": 15, "poly": [ 294.0, 1462.0, 1404.0, 1462.0, 1404.0, 1496.0, 294.0, 1496.0 ], "score": 0.973, "text": "contains 2O73 prompts for 177 datasets (11.7 prompts per dataset on average). Prompts used in ex-" }, { "category_id": 15, "poly": [ 292.0, 1489.0, 1404.0, 1489.0, 1404.0, 1527.0, 292.0, 1527.0 ], "score": 0.982, "text": " periments are all sourced from P3 except for BIG-bench,the prompts of which are provided by its" }, { "category_id": 15, "poly": [ 294.0, 1526.0, 440.0, 1526.0, 440.0, 1554.0, 294.0, 1554.0 ], "score": 1.0, "text": "maintainers." }, { "category_id": 15, "poly": [ 294.0, 1848.0, 1404.0, 1848.0, 1404.0, 1884.0, 294.0, 1884.0 ], "score": 0.99, "text": "All models we trained are based on T5,a Transformer-based encoder-decoder language model pre-" }, { "category_id": 15, "poly": [ 294.0, 1881.0, 1404.0, 1881.0, 1404.0, 1917.0, 294.0, 1917.0 ], "score": 0.994, "text": "trained with a masked language modeling-style objective on 1T tokens from C4 (Rafel et al., 2020)." }, { "category_id": 15, "poly": [ 293.0, 1908.0, 1407.0, 1908.0, 1407.0, 1948.0, 293.0, 1948.0 ], "score": 0.991, "text": " Since T5's pretraining objective is generating tokens and only tokens that have been removed from" }, { "category_id": 15, "poly": [ 294.0, 1942.0, 1404.0, 1942.0, 1404.0, 1978.0, 294.0, 1978.0 ], "score": 0.993, "text": "the input text, it is different from the natural text generation format of prompted datasets.Therefore," }, { "category_id": 15, "poly": [ 292.0, 1967.0, 622.0, 1967.0, 622.0, 2012.0, 292.0, 2012.0 ], "score": 0.989, "text": "we use Lester et al. (2021)'s" }, { "category_id": 15, "poly": [ 668.0, 1967.0, 770.0, 1967.0, 770.0, 2012.0, 668.0, 2012.0 ], "score": 0.976, "text": "-adapted" }, { "category_id": 15, "poly": [ 804.0, 1967.0, 1049.0, 1967.0, 1049.0, 2012.0, 804.0, 2012.0 ], "score": 0.998, "text": "model (referred to as" }, { "category_id": 15, "poly": [ 1148.0, 1967.0, 1407.0, 1967.0, 1407.0, 2012.0, 1148.0, 2012.0 ], "score": 0.949, "text": ", produced by training" }, { "category_id": 15, "poly": [ 293.0, 2000.0, 1220.0, 2000.0, 1220.0, 2037.0, 293.0, 2037.0 ], "score": 0.985, "text": "T5 on 1OoB additional tokens from C4 on a standard language modeling objective." }, { "category_id": 15, "poly": [ 294.0, 1680.0, 1407.0, 1680.0, 1407.0, 1717.0, 294.0, 1717.0 ], "score": 0.982, "text": "ModelAt a high level, we fine-tune a pretrained model on our multi-task training mixture of" }, { "category_id": 15, "poly": [ 293.0, 1712.0, 1406.0, 1712.0, 1406.0, 1747.0, 293.0, 1747.0 ], "score": 0.987, "text": " natural language prompted datasets. Our model uses an encoder-decoder architecture with input text" }, { "category_id": 15, "poly": [ 293.0, 1741.0, 1404.0, 1741.0, 1404.0, 1779.0, 293.0, 1779.0 ], "score": 0.986, "text": "fed to the encoder and target text produced by the decoder. The model is trained to autoregressively" }, { "category_id": 15, "poly": [ 293.0, 1770.0, 1408.0, 1770.0, 1408.0, 1815.0, 293.0, 1815.0 ], "score": 0.99, "text": " generate the target through standard maximum likelihood training. Unlike decoder-only language" }, { "category_id": 15, "poly": [ 293.0, 1800.0, 997.0, 1800.0, 997.0, 1840.0, 293.0, 1840.0 ], "score": 0.987, "text": "models such as GPT-3,it is never trained to generate the input." }, { "category_id": 15, "poly": [ 296.0, 1171.0, 1404.0, 1171.0, 1404.0, 1204.0, 296.0, 1204.0 ], "score": 0.991, "text": "Most of the prompts correspond directly to a version of the original proposed task, although we also" }, { "category_id": 15, "poly": [ 294.0, 1200.0, 1405.0, 1200.0, 1405.0, 1237.0, 294.0, 1237.0 ], "score": 0.986, "text": " allow prompts that permuted the original task (for instance, generating a document from its sum-" }, { "category_id": 15, "poly": [ 293.0, 1231.0, 1404.0, 1231.0, 1404.0, 1268.0, 293.0, 1268.0 ], "score": 0.997, "text": "mary). Such non-original-task prompts are included in our training mixtures for improved diversity," }, { "category_id": 15, "poly": [ 292.0, 1258.0, 1404.0, 1258.0, 1404.0, 1301.0, 292.0, 1301.0 ], "score": 0.989, "text": " but they are not reported in evaluation since they deviate from the metrics and baselines reported by" }, { "category_id": 15, "poly": [ 294.0, 1291.0, 533.0, 1291.0, 533.0, 1328.0, 294.0, 1328.0 ], "score": 0.977, "text": "the original datasets." }, { "category_id": 15, "poly": [ 293.0, 706.0, 1404.0, 706.0, 1404.0, 749.0, 293.0, 749.0 ], "score": 0.988, "text": " additional properties, such as an evaluation metric. 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}, { "category_id": 15, "poly": [ 293.0, 861.0, 1065.0, 861.0, 1065.0, 900.0, 293.0, 900.0 ], "score": 0.989, "text": " Each dot is the performance of one evaluation prompt. The baseline" }, { "category_id": 15, "poly": [ 1162.0, 861.0, 1407.0, 861.0, 1407.0, 900.0, 1162.0, 900.0 ], "score": 1.0, "text": "model is the same as" }, { "category_id": 15, "poly": [ 295.0, 890.0, 1404.0, 890.0, 1404.0, 929.0, 295.0, 929.0 ], "score": 0.99, "text": "TO except without multitask prompted training. GPT-3 only reports a single prompt for each dataset." }, { "category_id": 15, "poly": [ 294.0, 70.0, 818.0, 70.0, 818.0, 110.0, 294.0, 110.0 ], "score": 0.986, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 840.0, 2087.0, 861.0, 2087.0, 861.0, 2118.0, 840.0, 2118.0 ], "score": 1.0, "text": "6" }, { "category_id": 15, "poly": [ 294.0, 1591.0, 1404.0, 1591.0, 1404.0, 1628.0, 294.0, 1628.0 ], "score": 0.988, "text": "We truncate input and target sequences to 1024 and 256 tokens, respectively.Following Raffel et al." }, { "category_id": 15, "poly": [ 296.0, 1623.0, 1405.0, 1623.0, 1405.0, 1660.0, 296.0, 1660.0 ], "score": 0.987, "text": "(2020), we use packing to combine multiple training examples into a single sequence to reach the" }, { "category_id": 15, "poly": [ 291.0, 1649.0, 1262.0, 1649.0, 1262.0, 1690.0, 291.0, 1690.0 ], "score": 0.979, "text": " maximum sequence length. We use a batch size of IO24 sequences (corresponding to" }, { "category_id": 15, "poly": [ 1304.0, 1649.0, 1407.0, 1649.0, 1407.0, 1690.0, 1304.0, 1690.0 ], "score": 0.999, "text": "total in-" }, { "category_id": 15, "poly": [ 292.0, 1682.0, 1405.0, 1682.0, 1405.0, 1721.0, 292.0, 1721.0 ], "score": 0.988, "text": "put tokens per batch) and the Adafactor optimizer (Shazeer and Stern, 2Oi8).Following standard" }, { "category_id": 15, "poly": [ 293.0, 1713.0, 1225.0, 1713.0, 1225.0, 1750.0, 293.0, 1750.0 ], "score": 0.978, "text": " practice for fine-tuning T5,we use a learning rate of le-3 and a dropout rate of 0.1." }, { "category_id": 15, "poly": [ 296.0, 1392.0, 1404.0, 1392.0, 1404.0, 1428.0, 296.0, 1428.0 ], "score": 0.978, "text": "We assemble our multitask training mixture by combining and shufling all examples from al train-" }, { "category_id": 15, "poly": [ 294.0, 1424.0, 1406.0, 1424.0, 1406.0, 1460.0, 294.0, 1460.0 ], "score": 0.992, "text": "ing datasets.This is equivalent to sampling from each dataset in proportion to the number of ex-" }, { "category_id": 15, "poly": [ 293.0, 1453.0, 1404.0, 1453.0, 1404.0, 1490.0, 293.0, 1490.0 ], "score": 0.986, "text": "amples in the dataset. 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(2O2O) and treat" }, { "category_id": 15, "poly": [ 294.0, 1514.0, 544.0, 1514.0, 544.0, 1550.0, 294.0, 1550.0 ], "score": 0.976, "text": "any dataset with over" }, { "category_id": 15, "poly": [ 644.0, 1514.0, 873.0, 1514.0, 873.0, 1550.0, 644.0, 1550.0 ], "score": 0.998, "text": "examples as having" }, { "category_id": 15, "poly": [ 990.0, 1514.0, 1404.0, 1514.0, 1404.0, 1550.0, 990.0, 1550.0 ], "score": 0.999, "text": "num_templates examples for the" }, { "category_id": 15, "poly": [ 293.0, 1545.0, 1398.0, 1545.0, 1398.0, 1581.0, 293.0, 1581.0 ], "score": 0.977, "text": " purposes of sampling, where num_t emplat es is the number of templates created for the dataset." }, { "category_id": 15, "poly": [ 293.0, 1801.0, 1405.0, 1801.0, 1405.0, 1838.0, 293.0, 1838.0 ], "score": 0.991, "text": "EvaluationWe evaluate zero-shot generalization on 11 datasets in 4 held-out traditional NLP" }, { "category_id": 15, "poly": [ 295.0, 1835.0, 1405.0, 1835.0, 1405.0, 1868.0, 295.0, 1868.0 ], "score": 0.989, "text": "tasks: natural language inference, coreference, word sense disambiguation,and sentence completion," }, { "category_id": 15, "poly": [ 293.0, 1864.0, 1405.0, 1864.0, 1405.0, 1899.0, 293.0, 1899.0 ], "score": 0.986, "text": "as well as 14 novel tasks from BIG-bench (83). 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Lastly," }, { "category_id": 15, "poly": [ 840.0, 1039.0, 1405.0, 1039.0, 1405.0, 1073.0, 840.0, 1073.0 ], "score": 0.973, "text": "further adds SuperGLUE(Wang et al., 2019a) to" }, { "category_id": 15, "poly": [ 292.0, 1066.0, 1404.0, 1066.0, 1404.0, 1106.0, 292.0, 1106.0 ], "score": 0.988, "text": "the training mixture (except RTE and CB), which leaves NLI and the BIG-bench tasks as the only" }, { "category_id": 15, "poly": [ 293.0, 1100.0, 465.0, 1100.0, 465.0, 1133.0, 293.0, 1133.0 ], "score": 0.967, "text": "held-out tasks." }, { "category_id": 15, "poly": [ 296.0, 1147.0, 1155.0, 1147.0, 1155.0, 1180.0, 296.0, 1180.0 ], "score": 0.989, "text": "The above TO variants are all initialized from the 11B parameters version of" }, { "category_id": 15, "poly": [ 1250.0, 1147.0, 1404.0, 1147.0, 1404.0, 1180.0, 1250.0, 1180.0 ], "score": 0.995, "text": "To study the" }, { "category_id": 15, "poly": [ 295.0, 1176.0, 1405.0, 1176.0, 1405.0, 1210.0, 295.0, 1210.0 ], "score": 0.978, "text": "effect of scaling and to aid researchers with less resources, we also train TO (3B), which has the same" }, { "category_id": 15, "poly": [ 293.0, 1208.0, 1121.0, 1208.0, 1121.0, 1244.0, 293.0, 1244.0 ], "score": 0.991, "text": "training mixture as TO but is initialized from the 3B parameters version of" }, { "category_id": 15, "poly": [ 1216.0, 1208.0, 1405.0, 1208.0, 1405.0, 1244.0, 1216.0, 1244.0 ], "score": 0.996, "text": "(results reported" }, { "category_id": 15, "poly": [ 291.0, 1237.0, 484.0, 1237.0, 484.0, 1274.0, 291.0, 1274.0 ], "score": 0.998, "text": "in Appendix G)." }, { "category_id": 15, "poly": [ 295.0, 1283.0, 1405.0, 1283.0, 1405.0, 1321.0, 295.0, 1321.0 ], "score": 0.988, "text": "We perform checkpoint selection by choosing the checkpoint that yields the highest score on the" }, { "category_id": 15, "poly": [ 297.0, 1318.0, 1403.0, 1318.0, 1403.0, 1348.0, 297.0, 1348.0 ], "score": 0.983, "text": "validation splits of our training datasets.This still satisfies the true zero-shot (Perez et al.,2021)" }, { "category_id": 15, "poly": [ 294.0, 1346.0, 1392.0, 1346.0, 1392.0, 1383.0, 294.0, 1383.0 ], "score": 0.987, "text": " setting as we do not use any examples from any of the held-out tasks to select the best checkpoint." }, { "category_id": 15, "poly": [ 294.0, 1941.0, 1406.0, 1941.0, 1406.0, 1978.0, 294.0, 1978.0 ], "score": 0.993, "text": "For tasks that involve choosing the correct completion from several options (e.g. multiple choice" }, { "category_id": 15, "poly": [ 293.0, 1971.0, 1408.0, 1971.0, 1408.0, 2010.0, 293.0, 2010.0 ], "score": 0.981, "text": "question answering), we follow Brown et al. 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1405.0, 752.0, 1405.0, 786.0, 293.0, 786.0 ], "score": 0.993, "text": "Figure 5:Results for a subset of BIG-bench which has available baselines.The baseline models" }, { "category_id": 15, "poly": [ 295.0, 786.0, 1405.0, 786.0, 1405.0, 819.0, 295.0, 819.0 ], "score": 0.995, "text": "are Transformer-based language models provided by BIG-bench maintainers, who also provide one" }, { "category_id": 15, "poly": [ 293.0, 815.0, 569.0, 815.0, 569.0, 852.0, 293.0, 852.0 ], "score": 0.964, "text": " prompt per dataset. TO," }, { "category_id": 15, "poly": [ 624.0, 815.0, 675.0, 815.0, 675.0, 852.0, 624.0, 852.0 ], "score": 1.0, "text": "and" }, { "category_id": 15, "poly": [ 745.0, 815.0, 1405.0, 815.0, 1405.0, 852.0, 745.0, 852.0 ], "score": 0.999, "text": "are identical except for increasing the number of training" }, { "category_id": 15, "poly": [ 297.0, 847.0, 1140.0, 847.0, 1140.0, 876.0, 297.0, 876.0 ], "score": 0.975, "text": "datasets(β5).BIG-bench Tasks are all zero-shot forall the reported models." }, { "category_id": 15, "poly": [ 293.0, 1180.0, 485.0, 1180.0, 485.0, 1227.0, 293.0, 1227.0 ], "score": 0.988, "text": "6RESULTS" }, { "category_id": 15, "poly": [ 294.0, 1250.0, 815.0, 1250.0, 815.0, 1289.0, 294.0, 1289.0 ], "score": 0.997, "text": "6.1GENERALIZATION TO UNSEEN TASKS" }, { "category_id": 15, "poly": [ 294.0, 70.0, 818.0, 70.0, 818.0, 110.0, 294.0, 110.0 ], "score": 0.986, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 840.0, 2086.0, 860.0, 2086.0, 860.0, 2119.0, 840.0, 2119.0 ], "score": 1.0, "text": "7" }, { "category_id": 15, "poly": [ 840.0, 2086.0, 860.0, 2086.0, 860.0, 2119.0, 840.0, 2119.0 ], "score": 1.0, "text": "7" }, { "category_id": 15, "poly": [ 293.0, 1448.0, 1408.0, 1448.0, 1408.0, 1485.0, 293.0, 1485.0 ], "score": 0.982, "text": "Next, we compare TO to the zero-shot performance of the largest language models available as of" }, { "category_id": 15, "poly": [ 295.0, 1481.0, 1403.0, 1481.0, 1403.0, 1515.0, 295.0, 1515.0 ], "score": 0.974, "text": "writing,i.e., various GPT-3 models up to 175B parameters. Note that Brown et al. (2020) report per-" }, { "category_id": 15, "poly": [ 294.0, 1510.0, 1405.0, 1510.0, 1405.0, 1546.0, 294.0, 1546.0 ], "score": 0.995, "text": "formance on a single prompt,² whereas we report the median and interquartile range of performance" }, { "category_id": 15, "poly": [ 293.0, 1540.0, 1407.0, 1540.0, 1407.0, 1577.0, 293.0, 1577.0 ], "score": 0.985, "text": " across allprompts in P3 without cherry picking.We find that TO matches or exceeds the performance" }, { "category_id": 15, "poly": [ 294.0, 1570.0, 1405.0, 1570.0, 1405.0, 1606.0, 294.0, 1606.0 ], "score": 0.975, "text": "of all GPT-3 models on 9 out of 11 held-out datasets. Notably, neither TO nor GPT-3 is trained on" }, { "category_id": 15, "poly": [ 293.0, 1602.0, 1308.0, 1602.0, 1308.0, 1633.0, 293.0, 1633.0 ], "score": 0.994, "text": "natural language inference, yet TO outperforms GPT-3 on all NLI datasets, even though our" }, { "category_id": 15, "poly": [ 294.0, 1631.0, 1405.0, 1631.0, 1405.0, 1667.0, 294.0, 1667.0 ], "score": 0.995, "text": "baseline does not.The same is true for most datasets of other held-out tasks. The two exceptions are" }, { "category_id": 15, "poly": [ 295.0, 1659.0, 970.0, 1659.0, 970.0, 1698.0, 295.0, 1698.0 ], "score": 0.995, "text": "Winogrande and HellaSwag, which we discuss in Section 7." }, { "category_id": 15, "poly": [ 297.0, 985.0, 1403.0, 985.0, 1403.0, 1022.0, 297.0, 1022.0 ], "score": 0.994, "text": "We do not perform prompt selection by comparing the performance of different prompts on the vali-" }, { "category_id": 15, "poly": [ 293.0, 1014.0, 1405.0, 1014.0, 1405.0, 1052.0, 293.0, 1052.0 ], "score": 0.994, "text": "dation split; Perez et al. (2O21) highlights how such a strategy leaks information from the evaluation" }, { "category_id": 15, "poly": [ 292.0, 1044.0, 1407.0, 1044.0, 1407.0, 1084.0, 292.0, 1084.0 ], "score": 0.971, "text": " splits,which makes the evaluation not “true” zero-shot.For a given dataset, we report the median" }, { "category_id": 15, "poly": [ 295.0, 1078.0, 1405.0, 1078.0, 1405.0, 1115.0, 295.0, 1115.0 ], "score": 0.99, "text": "performance across all prompts for this dataset along with their interquartile range (Q3 - Q1) to" }, { "category_id": 15, "poly": [ 294.0, 1107.0, 997.0, 1107.0, 997.0, 1146.0, 294.0, 1146.0 ], "score": 0.991, "text": "measure the model's robustness to the wording of the prompts." }, { "category_id": 15, "poly": [ 295.0, 1707.0, 1295.0, 1707.0, 1295.0, 1743.0, 295.0, 1743.0 ], "score": 0.983, "text": "To evaluate our models on more unseen tasks, we assess the zero-shot performance of T0," }, { "category_id": 15, "poly": [ 1348.0, 1707.0, 1405.0, 1707.0, 1405.0, 1743.0, 1348.0, 1743.0 ], "score": 0.931, "text": ",and" }, { "category_id": 15, "poly": [ 366.0, 1738.0, 1408.0, 1738.0, 1408.0, 1774.0, 366.0, 1774.0 ], "score": 0.99, "text": "on a subset of BIG-bench (BIG-bench collboration,2O21). Tasks from BIG-bench cover a" }, { "category_id": 15, "poly": [ 295.0, 1770.0, 1404.0, 1770.0, 1404.0, 1806.0, 295.0, 1806.0 ], "score": 0.994, "text": "variety of novel skills not included in our training tasks,such as deducing the order of a sequence" }, { "category_id": 15, "poly": [ 293.0, 1801.0, 1403.0, 1801.0, 1403.0, 1837.0, 293.0, 1837.0 ], "score": 0.987, "text": "of objects, solving logic grid puzzles, and teling apart true statements from common misconcep-" }, { "category_id": 15, "poly": [ 293.0, 1831.0, 1406.0, 1831.0, 1406.0, 1867.0, 293.0, 1867.0 ], "score": 0.993, "text": " tions.The maintainers of BIG-bench provide a prompt for each dataset, with which we compare our" }, { "category_id": 15, "poly": [ 294.0, 1862.0, 1404.0, 1862.0, 1404.0, 1896.0, 294.0, 1896.0 ], "score": 0.987, "text": "models to a series of preliminary diagnostic baseline models trained by Google and evaluated by" }, { "category_id": 15, "poly": [ 294.0, 1893.0, 1405.0, 1893.0, 1405.0, 1927.0, 294.0, 1927.0 ], "score": 0.99, "text": "the BIG-bench maintainers.These models are decoder-only Transformer language models trained" }, { "category_id": 15, "poly": [ 293.0, 1925.0, 1405.0, 1925.0, 1405.0, 1956.0, 293.0, 1956.0 ], "score": 0.99, "text": "on a standard language modeling objective with varying model size.We find that at least one of the" }, { "category_id": 15, "poly": [ 294.0, 1311.0, 1403.0, 1311.0, 1403.0, 1345.0, 294.0, 1345.0 ], "score": 0.992, "text": "Our first research question is whether multitask prompted training improves generalization to unseen" }, { "category_id": 15, "poly": [ 295.0, 1343.0, 802.0, 1343.0, 802.0, 1375.0, 295.0, 1375.0 ], "score": 0.984, "text": "tasks.In Figure 4,we compare TO against our" }, { "category_id": 15, "poly": [ 897.0, 1343.0, 1405.0, 1343.0, 1405.0, 1375.0, 897.0, 1375.0 ], "score": 0.981, "text": "baseline on four held-out tasks. Our approach" }, { "category_id": 15, "poly": [ 293.0, 1372.0, 1407.0, 1372.0, 1407.0, 1407.0, 293.0, 1407.0 ], "score": 0.99, "text": "leads to significant gains over our baseline on all datasets,demonstrating the benefits of multitask" }, { "category_id": 15, "poly": [ 293.0, 1403.0, 1344.0, 1403.0, 1344.0, 1438.0, 293.0, 1438.0 ], "score": 0.992, "text": " prompted training over only language modeling training with an identical model and prompts." }, { "category_id": 15, "poly": [ 292.0, 901.0, 1402.0, 901.0, 1402.0, 950.0, 292.0, 950.0 ], "score": 0.968, "text": "select the option with the highest log-likelihood as the prediction.For simplicity,we do not apply" }, { "category_id": 15, "poly": [ 295.0, 939.0, 1011.0, 939.0, 1011.0, 974.0, 295.0, 974.0 ], "score": 0.989, "text": "length normalization to the log-likelihoods of the target options." }, { "category_id": 15, "poly": [ 293.0, 752.0, 1405.0, 752.0, 1405.0, 786.0, 293.0, 786.0 ], "score": 0.993, "text": "Figure 5:Results for a subset of BIG-bench which has available baselines.The baseline models" }, { "category_id": 15, "poly": [ 295.0, 786.0, 1405.0, 786.0, 1405.0, 819.0, 295.0, 819.0 ], "score": 0.995, "text": "are Transformer-based language models provided by BIG-bench maintainers, who also provide one" }, { "category_id": 15, "poly": [ 293.0, 815.0, 569.0, 815.0, 569.0, 852.0, 293.0, 852.0 ], "score": 0.964, "text": " prompt per dataset. 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Zero-shot performance of TO and" }, { "category_id": 15, "poly": [ 1296.0, 876.0, 1404.0, 876.0, 1404.0, 913.0, 1296.0, 913.0 ], "score": 0.998, "text": "when in-" }, { "category_id": 15, "poly": [ 293.0, 906.0, 1405.0, 906.0, 1405.0, 942.0, 293.0, 942.0 ], "score": 0.992, "text": "creasing number of training prompts per dataset. Each dot is the performance of one evaluation" }, { "category_id": 15, "poly": [ 293.0, 935.0, 624.0, 935.0, 624.0, 976.0, 293.0, 976.0 ], "score": 0.967, "text": "prompt. The main TO model " }, { "category_id": 15, "poly": [ 735.0, 935.0, 1406.0, 935.0, 1406.0, 976.0, 735.0, 976.0 ], "score": 0.99, "text": " includes non-original-task prompts (see Section 3). 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TO was trained on some prompts that do not map onto the" }, { "category_id": 15, "poly": [ 293.0, 1446.0, 1405.0, 1446.0, 1405.0, 1484.0, 293.0, 1484.0 ], "score": 0.98, "text": "dataset's original task,for example “given an answer, generate a plausible question\". Including these" }, { "category_id": 15, "poly": [ 292.0, 1477.0, 500.0, 1477.0, 500.0, 1515.0, 292.0, 1515.0 ], "score": 0.987, "text": " prompts results in" }, { "category_id": 15, "poly": [ 521.0, 1477.0, 1406.0, 1477.0, 1406.0, 1515.0, 521.0, 1515.0 ], "score": 0.979, "text": "being 8.O3 on average (which corresponds to our main TO model). 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Figure 6 shows that, even with just one prompt per dataset, performance on unseen tasks" }, { "category_id": 15, "poly": [ 292.0, 1627.0, 1405.0, 1627.0, 1405.0, 1668.0, 292.0, 1668.0 ], "score": 0.984, "text": " can improve substantially over the non-prompted baseline, although the spread (interquartile range" }, { "category_id": 15, "poly": [ 292.0, 1656.0, 946.0, 1656.0, 946.0, 1699.0, 292.0, 1699.0 ], "score": 0.987, "text": "between Q1 and Q3) does not consistently improve with" }, { "category_id": 15, "poly": [ 1024.0, 1656.0, 1382.0, 1656.0, 1382.0, 1699.0, 1024.0, 1699.0 ], "score": 0.984, "text": ".Meanwhile, further increasing" }, { "category_id": 16, "poly": [ 1403.0, 1656.0, 1408.0, 1656.0, 1408.0, 1699.0, 1403.0, 1699.0 ], "score": 0.736, "text": "（20" }, { "category_id": 15, "poly": [ 292.0, 1688.0, 1405.0, 1688.0, 1405.0, 1727.0, 292.0, 1727.0 ], "score": 0.989, "text": "from 1 to an average of 5.7 does yield additional improvement in both median (increases for 8/11" }, { "category_id": 15, "poly": [ 293.0, 1719.0, 1404.0, 1719.0, 1404.0, 1757.0, 293.0, 1757.0 ], "score": 0.979, "text": "datasets)and spread (decreases for 7/11 datasets). This reinforces our hypothesis that training on" }, { "category_id": 15, "poly": [ 292.0, 1751.0, 1405.0, 1751.0, 1405.0, 1788.0, 292.0, 1788.0 ], "score": 0.988, "text": "more prompts per dataset leads to better and more robust generalization to unseen tasks.Finally," }, { "category_id": 15, "poly": [ 294.0, 1782.0, 1404.0, 1782.0, 1404.0, 1816.0, 294.0, 1816.0 ], "score": 0.984, "text": "we find that TO's inclusion all prompts (including those that do not correspond to the dataset's" }, { "category_id": 15, "poly": [ 294.0, 1813.0, 1406.0, 1813.0, 1406.0, 1847.0, 294.0, 1847.0 ], "score": 0.981, "text": "original task) further improves the median (increases for 9/11 datasets) and spread (decreases for" }, { "category_id": 15, "poly": [ 292.0, 1840.0, 1288.0, 1840.0, 1288.0, 1879.0, 292.0, 1879.0 ], "score": 0.99, "text": "8/11 datasets), showing that training on non-original-task prompts can also be beneficial." }, { "category_id": 15, "poly": [ 294.0, 1909.0, 1067.0, 1909.0, 1067.0, 1947.0, 294.0, 1947.0 ], "score": 0.989, "text": "Effect of Prompts from More DatasetsIn this experiment, we fix" }, { "category_id": 15, "poly": [ 1151.0, 1909.0, 1407.0, 1909.0, 1407.0, 1947.0, 1151.0, 1947.0 ], "score": 0.999, "text": "available prompts and" }, { "category_id": 15, "poly": [ 294.0, 1941.0, 396.0, 1941.0, 396.0, 1977.0, 294.0, 1977.0 ], "score": 1.0, "text": "increase" }, { "category_id": 15, "poly": [ 417.0, 1941.0, 710.0, 1941.0, 710.0, 1977.0, 417.0, 1977.0 ], "score": 0.949, "text": "from 39 to 49 to 55 (T0," }, { "category_id": 16, "poly": [ 762.0, 1941.0, 772.0, 1941.0, 772.0, 1977.0, 762.0, 1977.0 ], "score": 0.0, "text": "" }, { "category_id": 15, "poly": [ 840.0, 1941.0, 1406.0, 1941.0, 1406.0, 1977.0, 840.0, 1977.0 ], "score": 0.978, "text": ",respectively. See Section 5 for details.)Figure 7" }, { "category_id": 15, "poly": [ 295.0, 1973.0, 1110.0, 1973.0, 1110.0, 2005.0, 295.0, 2005.0 ], "score": 0.993, "text": "shows that the median performance of all 5 held-out datasets increases as" }, { "category_id": 15, "poly": [ 1130.0, 1973.0, 1404.0, 1973.0, 1404.0, 2005.0, 1130.0, 2005.0 ], "score": 0.995, "text": "increases from 39 to 49." }, { "category_id": 15, "poly": [ 294.0, 2000.0, 1406.0, 2000.0, 1406.0, 2039.0, 294.0, 2039.0 ], "score": 0.992, "text": "However, the spread only decreases for 1 out of 5 datasets.For some datasets (e.g., ANLI), this is an" }, { "category_id": 15, "poly": [ 294.0, 1254.0, 1406.0, 1254.0, 1406.0, 1294.0, 294.0, 1294.0 ], "score": 0.99, "text": "Our second research question is whether training on a wider range of prompts improves robustness" }, { "category_id": 15, "poly": [ 293.0, 1285.0, 1406.0, 1285.0, 1406.0, 1325.0, 293.0, 1325.0 ], "score": 0.989, "text": " to the wording of the prompts. We conduct two ablation experiments on the effects of the average" }, { "category_id": 15, "poly": [ 294.0, 1315.0, 642.0, 1315.0, 642.0, 1355.0, 294.0, 1355.0 ], "score": 0.999, "text": "number of prompts per dataset" }, { "category_id": 15, "poly": [ 679.0, 1315.0, 986.0, 1315.0, 986.0, 1355.0, 679.0, 1355.0 ], "score": 0.984, "text": "and the number of datasets" }, { "category_id": 15, "poly": [ 1022.0, 1315.0, 1260.0, 1315.0, 1260.0, 1355.0, 1022.0, 1355.0 ], "score": 0.969, "text": "used during training." }, { "category_id": 15, "poly": [ 293.0, 1062.0, 1405.0, 1062.0, 1405.0, 1102.0, 293.0, 1102.0 ], "score": 0.991, "text": "TO variants outperform all baseline models on alltasks except for StrategyQA (Figure 5). 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846.0, 525.0, 843.0, 525.0 ], "score": 0.677, "text": "（2" }, { "category_id": 15, "poly": [ 906.0, 492.0, 953.0, 492.0, 953.0, 525.0, 906.0, 525.0 ], "score": 0.994, "text": "□" }, { "category_id": 16, "poly": [ 1072.0, 492.0, 1077.0, 492.0, 1077.0, 525.0, 1072.0, 525.0 ], "score": 0.68, "text": "（204号" }, { "category_id": 15, "poly": [ 328.0, 1944.0, 1407.0, 1944.0, 1407.0, 1982.0, 328.0, 1982.0 ], "score": 0.975, "text": "3https://beta.openai.com/ We use the“base GPT-3 model\" davinci. Although OpenAI does not disclose" }, { "category_id": 15, "poly": [ 295.0, 1978.0, 1404.0, 1978.0, 1404.0, 2010.0, 295.0, 2010.0 ], "score": 0.979, "text": "which one of their commercially available models correspond to which models reported in Brown et al. (2020)," }, { "category_id": 15, "poly": [ 297.0, 2005.0, 1038.0, 2005.0, 1038.0, 2034.0, 297.0, 2034.0 ], "score": 0.97, "text": "Gao et al. (2021) estimate that davinci corresponds to the 175B model." }, { "category_id": 15, "poly": [ 292.0, 596.0, 1405.0, 596.0, 1405.0, 640.0, 292.0, 640.0 ], "score": 0.991, "text": " Figure 7: Efect of prompts from more datasets. Zero-shot performance of three models with varying" }, { "category_id": 15, "poly": [ 294.0, 632.0, 579.0, 632.0, 579.0, 666.0, 294.0, 666.0 ], "score": 0.95, "text": "number of datasets （TO," }, { "category_id": 16, "poly": [ 632.0, 632.0, 642.0, 632.0, 642.0, 666.0, 632.0, 666.0 ], "score": 0.43, "text": "，" }, { "category_id": 15, "poly": [ 715.0, 632.0, 1404.0, 632.0, 1404.0, 666.0, 715.0, 666.0 ], "score": 0.983, "text": ".Adding more datasets consistently leads to higher median" }, { "category_id": 15, "poly": [ 296.0, 663.0, 1152.0, 663.0, 1152.0, 697.0, 296.0, 697.0 ], "score": 0.992, "text": "performance but does not always reduce interquartile range for unseen tasks." }, { "category_id": 15, "poly": [ 293.0, 1220.0, 698.0, 1220.0, 698.0, 1266.0, 293.0, 1266.0 ], "score": 0.995, "text": "7COMPARISON TO FLAN" }, { "category_id": 15, "poly": [ 291.0, 1619.0, 550.0, 1619.0, 550.0, 1670.0, 291.0, 1670.0 ], "score": 0.988, "text": "8CONCLUSION" }, { "category_id": 15, "poly": [ 295.0, 72.0, 818.0, 72.0, 818.0, 110.0, 295.0, 110.0 ], "score": 0.989, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 839.0, 2087.0, 860.0, 2087.0, 860.0, 2117.0, 839.0, 2117.0 ], "score": 1.0, "text": "9" }, { "category_id": 15, "poly": [ 296.0, 1298.0, 1406.0, 1298.0, 1406.0, 1334.0, 296.0, 1334.0 ], "score": 0.982, "text": "Concurrent to our work, Wei et al. (2O21) proposes FLAN, which shares largely the same method of" }, { "category_id": 15, "poly": [ 296.0, 1329.0, 1405.0, 1329.0, 1405.0, 1362.0, 296.0, 1362.0 ], "score": 0.982, "text": "enabling zero-shot generalization through multitask prompted training. With a mixture of datasets" }, { "category_id": 15, "poly": [ 293.0, 1359.0, 1406.0, 1359.0, 1406.0, 1396.0, 293.0, 1396.0 ], "score": 0.987, "text": " similar to ours, they train multiple decoder-only language models,each with a single held-out task" }, { "category_id": 15, "poly": [ 296.0, 1391.0, 1405.0, 1391.0, 1405.0, 1423.0, 296.0, 1423.0 ], "score": 0.991, "text": "(cf. we focus on training one model with multiple held-out tasks in order to evaluate the model's" }, { "category_id": 15, "poly": [ 294.0, 1419.0, 1406.0, 1419.0, 1406.0, 1456.0, 294.0, 1456.0 ], "score": 0.975, "text": "ability to generalize to diverse tasks.） Compared to FLAN, TO's zero-shot performance is better" }, { "category_id": 15, "poly": [ 293.0, 1448.0, 1333.0, 1448.0, 1333.0, 1485.0, 293.0, 1485.0 ], "score": 0.988, "text": "on CB and RTE, similar on Story Cloze and COPA,and worse on Winogrande and ANLI." }, { "category_id": 15, "poly": [ 293.0, 1481.0, 1406.0, 1481.0, 1406.0, 1515.0, 293.0, 1515.0 ], "score": 0.995, "text": "outperforms FLAN on CB,RTE,and COPA and matches FLAN's performance on Winogrande and" }, { "category_id": 15, "poly": [ 293.0, 1509.0, 568.0, 1509.0, 568.0, 1545.0, 293.0, 1545.0 ], "score": 0.992, "text": "ANLI. Notably, TO and" }, { "category_id": 15, "poly": [ 638.0, 1509.0, 1121.0, 1509.0, 1121.0, 1545.0, 638.0, 1545.0 ], "score": 0.991, "text": "attain this performance despite being over" }, { "category_id": 15, "poly": [ 1175.0, 1509.0, 1405.0, 1509.0, 1405.0, 1545.0, 1175.0, 1545.0 ], "score": 0.973, "text": " smaller than FLAN" }, { "category_id": 15, "poly": [ 296.0, 1542.0, 1146.0, 1542.0, 1146.0, 1575.0, 296.0, 1575.0 ], "score": 0.98, "text": "(137B vs.11B parameters).Appendix B discusses our differences in details." }, { "category_id": 15, "poly": [ 295.0, 924.0, 1406.0, 924.0, 1406.0, 964.0, 295.0, 964.0 ], "score": 0.969, "text": "Comparing TO and GPT-3's robustnessBecause Brown et al. (2O2O) only report one prompt" }, { "category_id": 15, "poly": [ 292.0, 956.0, 1131.0, 956.0, 1131.0, 993.0, 292.0, 993.0 ], "score": 0.992, "text": " per dataset with no standard deviation,we evaluate GPT-3 via OpenAI's" }, { "category_id": 15, "poly": [ 1192.0, 956.0, 1406.0, 956.0, 1406.0, 993.0, 1192.0, 993.0 ], "score": 0.988, "text": "on RTE using the" }, { "category_id": 15, "poly": [ 293.0, 987.0, 1408.0, 987.0, 1408.0, 1024.0, 293.0, 1024.0 ], "score": 0.982, "text": "same 10 prompts we evaluate TO in order to estimate GPT-3 robustness’to different wording of" }, { "category_id": 15, "poly": [ 292.0, 1018.0, 1405.0, 1018.0, 1405.0, 1054.0, 292.0, 1054.0 ], "score": 0.976, "text": " prompts. One of these templates is identical to Brown et al. (2020, p. 59)s reported prompt, which" }, { "category_id": 15, "poly": [ 292.0, 1050.0, 552.0, 1050.0, 552.0, 1084.0, 292.0, 1084.0 ], "score": 0.986, "text": "scores an accuracy of" }, { "category_id": 15, "poly": [ 628.0, 1050.0, 813.0, 1050.0, 813.0, 1084.0, 628.0, 1084.0 ], "score": 0.972, "text": ",lower than the" }, { "category_id": 15, "poly": [ 891.0, 1050.0, 1407.0, 1050.0, 1407.0, 1084.0, 891.0, 1084.0 ], "score": 0.967, "text": "reported in Brown et al. (2020).All other 9" }, { "category_id": 15, "poly": [ 293.0, 1079.0, 1282.0, 1079.0, 1282.0, 1115.0, 293.0, 1115.0 ], "score": 0.982, "text": " prompts, however, yield roughly random-guessing performance with median accuracy" }, { "category_id": 16, "poly": [ 1402.0, 1079.0, 1406.0, 1079.0, 1406.0, 1115.0, 1402.0, 1115.0 ], "score": 0.708, "text": "（204号" }, { "category_id": 15, "poly": [ 293.0, 1109.0, 562.0, 1109.0, 562.0, 1147.0, 293.0, 1147.0 ], "score": 0.986, "text": " and interquartile range" }, { "category_id": 15, "poly": [ 667.0, 1109.0, 1405.0, 1109.0, 1405.0, 1147.0, 667.0, 1147.0 ], "score": 0.998, "text": ". These results suggest that TO could be more robust to prompt" }, { "category_id": 15, "poly": [ 296.0, 1142.0, 577.0, 1142.0, 577.0, 1172.0, 296.0, 1172.0 ], "score": 0.994, "text": "formulation than GPT-3." }, { "category_id": 15, "poly": [ 295.0, 1698.0, 1404.0, 1698.0, 1404.0, 1735.0, 295.0, 1735.0 ], "score": 0.993, "text": "We demonstrate that multitask prompted training can enable strong zero-shot generalization abilities" }, { "category_id": 15, "poly": [ 294.0, 1730.0, 1405.0, 1730.0, 1405.0, 1764.0, 294.0, 1764.0 ], "score": 0.987, "text": " in language models. This approach provides an effective alternative to unsupervised language model" }, { "category_id": 15, "poly": [ 293.0, 1761.0, 1404.0, 1761.0, 1404.0, 1794.0, 293.0, 1794.0 ], "score": 0.99, "text": "pretraining,often enabling our TO model to outperform models many times its size. We also perform" }, { "category_id": 15, "poly": [ 293.0, 1790.0, 1406.0, 1790.0, 1406.0, 1825.0, 293.0, 1825.0 ], "score": 0.989, "text": " ablation studies demonstrating the importance of including many diverse prompts and the impact" }, { "category_id": 15, "poly": [ 293.0, 1820.0, 1405.0, 1820.0, 1405.0, 1856.0, 293.0, 1856.0 ], "score": 0.994, "text": "of increasing the number of datasets in each task. To enable future work on improving zero-shot" }, { "category_id": 15, "poly": [ 293.0, 1852.0, 1405.0, 1852.0, 1405.0, 1889.0, 293.0, 1889.0 ], "score": 0.981, "text": " generalization, we release all models trained in this paper in addition to the collction of prompts" }, { "category_id": 15, "poly": [ 296.0, 1884.0, 781.0, 1884.0, 781.0, 1914.0, 296.0, 1914.0 ], "score": 0.971, "text": "we created and our prompt annotation tool." }, { "category_id": 15, "poly": [ 293.0, 730.0, 1405.0, 730.0, 1405.0, 769.0, 293.0, 769.0 ], "score": 0.989, "text": " artifact of the fact that some prompts always perform poorly, so that when other prompts improve," }, { "category_id": 15, "poly": [ 296.0, 763.0, 1402.0, 763.0, 1402.0, 796.0, 296.0, 796.0 ], "score": 0.984, "text": "the spread is stretched larger. For other datasets (e.g., CB), however, the spread does decrease with" }, { "category_id": 15, "poly": [ 348.0, 790.0, 396.0, 790.0, 396.0, 829.0, 348.0, 829.0 ], "score": 0.85, "text": ".As" }, { "category_id": 15, "poly": [ 417.0, 790.0, 1405.0, 790.0, 1405.0, 829.0, 417.0, 829.0 ], "score": 0.985, "text": "increases from 49 to 55, the median performance of all datasets again increases, but the" }, { "category_id": 15, "poly": [ 292.0, 821.0, 1407.0, 821.0, 1407.0, 862.0, 292.0, 862.0 ], "score": 0.989, "text": " spread only decreases for 2 out of 5 datasets. Although further investigation is needed, it appears" }, { "category_id": 15, "poly": [ 293.0, 851.0, 465.0, 851.0, 465.0, 891.0, 293.0, 891.0 ], "score": 0.984, "text": "that increasing" }, { "category_id": 15, "poly": [ 486.0, 851.0, 1351.0, 851.0, 1351.0, 891.0, 486.0, 891.0 ], "score": 0.996, "text": "does not consistently make the model more robust to the wording of prompts." } ], "page_info": { "page_no": 8, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 295, 1404, 295, 1404, 508, 298, 508 ], "score": 0.971 }, { "category_id": 1, "poly": [ 298, 662, 1403, 662, 1403, 786, 298, 786 ], "score": 0.965 }, { "category_id": 1, "poly": [ 300, 524, 1403, 524, 1403, 646, 300, 646 ], "score": 0.961 }, { "category_id": 2, "poly": [ 300, 76, 815, 76, 815, 104, 300, 104 ], "score": 0.889 }, { "category_id": 0, "poly": [ 300, 831, 488, 831, 488, 863, 300, 863 ], "score": 0.881 }, { "category_id": 2, "poly": [ 837, 2088, 865, 2088, 865, 2112, 837, 2112 ], "score": 0.829 }, { "category_id": 0, "poly": [ 302, 229, 625, 229, 625, 260, 302, 260 ], "score": 0.82 }, { "category_id": 1, "poly": [ 301, 1941, 1402, 1941, 1402, 2033, 301, 2033 ], "score": 0.788 }, { "category_id": 1, "poly": [ 298, 1860, 1399, 1860, 1399, 1924, 298, 1924 ], "score": 0.755 }, { "category_id": 1, "poly": [ 291, 1637, 1402, 1637, 1402, 1702, 291, 1702 ], "score": 0.725 }, { "category_id": 1, "poly": [ 296, 1446, 1397, 1446, 1397, 1511, 296, 1511 ], "score": 0.712 }, { "category_id": 1, "poly": [ 296, 1305, 1403, 1305, 1403, 1429, 296, 1429 ], "score": 0.708 }, { "category_id": 1, "poly": [ 297, 1718, 1404, 1718, 1404, 1844, 297, 1844 ], "score": 0.674 }, { "category_id": 1, "poly": [ 293, 1083, 1400, 1083, 1400, 1147, 293, 1147 ], "score": 0.67 }, { "category_id": 1, "poly": [ 298, 1527, 1401, 1527, 1401, 1622, 298, 1622 ], "score": 0.651 }, { "category_id": 1, "poly": [ 297, 1163, 1404, 1163, 1404, 1287, 297, 1287 ], "score": 0.624 }, { "category_id": 1, "poly": [ 300, 881, 1405, 881, 1405, 1066, 300, 1066 ], "score": 0.615 }, { "category_id": 15, "poly": [ 296.0, 73.0, 817.0, 73.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.992, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 296.0, 831.0, 490.0, 831.0, 490.0, 867.0, 296.0, 867.0 ], "score": 0.99, "text": "REFERENCES" }, { "category_id": 15, "poly": [ 831.0, 2084.0, 871.0, 2084.0, 871.0, 2125.0, 831.0, 2125.0 ], "score": 0.999, "text": "10" }, { "category_id": 15, "poly": [ 300.0, 229.0, 628.0, 229.0, 628.0, 265.0, 300.0, 265.0 ], "score": 0.997, "text": "ACKNOWLEDGEMENTS" }, { "category_id": 15, "poly": [ 293.0, 294.0, 1405.0, 294.0, 1405.0, 331.0, 293.0, 331.0 ], "score": 0.993, "text": "This work was granted access to the HPC resources of Institut du développement et des ressources en" }, { "category_id": 15, "poly": [ 294.0, 326.0, 1405.0, 326.0, 1405.0, 360.0, 294.0, 360.0 ], "score": 0.991, "text": "informatique scientifique (IDRIS) du Centre national de la recherche scientifique (CNRS) under the" }, { "category_id": 15, "poly": [ 294.0, 356.0, 1404.0, 356.0, 1404.0, 390.0, 294.0, 390.0 ], "score": 0.982, "text": "allocation 2021-A0101012475 made by Grand équipement national de calcul intensif (GENCI). In" }, { "category_id": 15, "poly": [ 292.0, 386.0, 1406.0, 386.0, 1406.0, 422.0, 292.0, 422.0 ], "score": 0.979, "text": " particular, allthe evaluations and data processing ran on the Jean-Zay cluster of IDRIS,and we want" }, { "category_id": 15, "poly": [ 294.0, 417.0, 1404.0, 417.0, 1404.0, 452.0, 294.0, 452.0 ], "score": 0.992, "text": "to thank the IDRIS team for responsive support throughout the project, in particular Rémi Lacroix." }, { "category_id": 15, "poly": [ 293.0, 446.0, 1408.0, 446.0, 1408.0, 485.0, 293.0, 485.0 ], "score": 0.99, "text": "We are grateful for the TPU Research Cloud program which generously provided TPU credits to" }, { "category_id": 15, "poly": [ 294.0, 479.0, 1170.0, 479.0, 1170.0, 513.0, 294.0, 513.0 ], "score": 0.987, "text": "Hugging Face. Those credits were used to train all the models from this paper." }, { "category_id": 15, "poly": [ 293.0, 660.0, 1405.0, 660.0, 1405.0, 698.0, 293.0, 698.0 ], "score": 0.975, "text": "We thank Yacine Jernite, Sasha Luccioni, Aurelie Néveol and Huu Nguyen for advising on strategies" }, { "category_id": 15, "poly": [ 294.0, 692.0, 1404.0, 692.0, 1404.0, 725.0, 294.0, 725.0 ], "score": 0.984, "text": "to deal with datasets containing potentially harmful content. Guy Gur-Ari and Ethan Dyer provided" }, { "category_id": 15, "poly": [ 293.0, 726.0, 1403.0, 726.0, 1403.0, 757.0, 293.0, 757.0 ], "score": 0.985, "text": "assistance and preliminary results on BIG-bench evaluation.We thank Ruiqi Zhong for early dis-" }, { "category_id": 15, "poly": [ 294.0, 757.0, 569.0, 757.0, 569.0, 789.0, 294.0, 789.0 ], "score": 0.994, "text": "cussions on this project." }, { "category_id": 15, "poly": [ 295.0, 521.0, 1403.0, 521.0, 1403.0, 558.0, 295.0, 558.0 ], "score": 0.987, "text": "This work was partly funded by Rachel Bawden and Benoit Sagot's chairs in the PRAIRIE institute" }, { "category_id": 15, "poly": [ 295.0, 554.0, 1403.0, 554.0, 1403.0, 590.0, 295.0, 590.0 ], "score": 0.985, "text": "funded by the French national agency ANR as part of the “Investissements d'avenir” programme" }, { "category_id": 15, "poly": [ 295.0, 586.0, 1404.0, 586.0, 1404.0, 619.0, 295.0, 619.0 ], "score": 0.969, "text": "under the reference ANR-19-P3IA-Oo01. Disclosure: Stephen Bach contributed to this work as an" }, { "category_id": 15, "poly": [ 295.0, 616.0, 551.0, 616.0, 551.0, 649.0, 295.0, 649.0 ], "score": 0.993, "text": "advisor to Snorkel AI." }, { "category_id": 15, "poly": [ 294.0, 1934.0, 1407.0, 1934.0, 1407.0, 1981.0, 294.0, 1981.0 ], "score": 0.975, "text": "Yonatan Bisk, Rowan Zelers,Ronan Le Bras,Jianfeng Gao, and Yejin Choi.Piqa: Reasoning" }, { "category_id": 15, "poly": [ 324.0, 1973.0, 1404.0, 1973.0, 1404.0, 2006.0, 324.0, 2006.0 ], "score": 0.991, "text": "about physical commonsense in natural language. In Thirty-Fourth AAAI Conference on Artificial" }, { "category_id": 15, "poly": [ 324.0, 2001.0, 537.0, 2001.0, 537.0, 2036.0, 324.0, 2036.0 ], "score": 0.985, "text": "Intelligence,2020." }, { "category_id": 15, "poly": [ 294.0, 1860.0, 1404.0, 1860.0, 1404.0, 1896.0, 294.0, 1896.0 ], "score": 0.982, "text": "BIG-bench collaboration. 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Victor Sanh co-led the prompt engieering" }, { "category_id": 15, "poly": [ 293.0, 1532.0, 1406.0, 1532.0, 1406.0, 1567.0, 293.0, 1567.0 ], "score": 0.986, "text": " group, managed the prompt collection procedure, implemented the prompt materialization, and ran" }, { "category_id": 15, "poly": [ 293.0, 1562.0, 1405.0, 1562.0, 1405.0, 1594.0, 293.0, 1594.0 ], "score": 0.986, "text": "evaluation systems. Albert Webson reviewed and selected alltraining and evaluation datasets, led" }, { "category_id": 15, "poly": [ 294.0, 1592.0, 1404.0, 1592.0, 1404.0, 1625.0, 294.0, 1625.0 ], "score": 0.988, "text": "the analysis of results,designed the ablation studies,and co-managed the writing proces. Colin" }, { "category_id": 15, "poly": [ 294.0, 1623.0, 1405.0, 1623.0, 1405.0, 1657.0, 294.0, 1657.0 ], "score": 0.985, "text": "Raffel proposed the research direction,trained all the models, named the model,and built the main" }, { "category_id": 15, "poly": [ 293.0, 1652.0, 1404.0, 1652.0, 1404.0, 1689.0, 293.0, 1689.0 ], "score": 0.983, "text": "evaluation system. Stephen Bach co-led the prompt engineering group, developed the prompting" }, { "category_id": 15, "poly": [ 294.0, 1684.0, 1405.0, 1684.0, 1405.0, 1718.0, 294.0, 1718.0 ], "score": 0.987, "text": "tool and guidelines,and led the prompt collection effort central to the work.Additionally,Alexander" }, { "category_id": 15, "poly": [ 293.0, 1713.0, 1349.0, 1713.0, 1349.0, 1750.0, 293.0, 1750.0 ], "score": 0.985, "text": " Rush helped develop the prompt templating language and tool, and co-managed paper writing." }, { "category_id": 15, "poly": [ 294.0, 1269.0, 1405.0, 1269.0, 1405.0, 1307.0, 294.0, 1307.0 ], "score": 0.996, "text": "This research was conducted under the BigScience project for open research,4 a year-long initiative" }, { "category_id": 15, "poly": [ 293.0, 1301.0, 1405.0, 1301.0, 1405.0, 1339.0, 293.0, 1339.0 ], "score": 0.99, "text": " targeting the study of large models and datasets. The goal of the project is to research language" }, { "category_id": 15, "poly": [ 293.0, 1330.0, 1405.0, 1330.0, 1405.0, 1369.0, 293.0, 1369.0 ], "score": 0.973, "text": " models in a public environment outside large technology companies.The project has 6OO researchers" }, { "category_id": 15, "poly": [ 294.0, 1363.0, 1404.0, 1363.0, 1404.0, 1397.0, 294.0, 1397.0 ], "score": 0.98, "text": "from 50 countries and more than 25O institutions. The BigScience project was initiated by Thomas" }, { "category_id": 15, "poly": [ 293.0, 1390.0, 1404.0, 1390.0, 1404.0, 1427.0, 293.0, 1427.0 ], "score": 0.981, "text": "Wolf at Hugging Face, and this collaboration would not have been possible without his effort. This" }, { "category_id": 15, "poly": [ 291.0, 1422.0, 1405.0, 1422.0, 1405.0, 1459.0, 291.0, 1459.0 ], "score": 0.984, "text": "research was the focus of the BigScience Prompt Engineering working group, which focused on the" }, { "category_id": 15, "poly": [ 293.0, 1453.0, 872.0, 1453.0, 872.0, 1490.0, 293.0, 1490.0 ], "score": 0.962, "text": " role of prompting in large language model training." }, { "category_id": 15, "poly": [ 294.0, 1761.0, 1403.0, 1761.0, 1403.0, 1795.0, 294.0, 1795.0 ], "score": 0.988, "text": "Following the goals of the BigScience project, this work is co-authored by all contributors to the" }, { "category_id": 15, "poly": [ 292.0, 1790.0, 1407.0, 1790.0, 1407.0, 1827.0, 292.0, 1827.0 ], "score": 0.994, "text": "working group. We define this contribution as having contributed at least 3 accepted prompted" }, { "category_id": 15, "poly": [ 294.0, 1822.0, 1405.0, 1822.0, 1405.0, 1857.0, 294.0, 1857.0 ], "score": 0.987, "text": "datasets to the project. Lacking a better metric,authors are sorted based on code contributions to" }, { "category_id": 15, "poly": [ 294.0, 1853.0, 1404.0, 1853.0, 1404.0, 1887.0, 294.0, 1887.0 ], "score": 0.985, "text": "the project. We explicitly highlight the work of: Lintang Sutawika, who helped with evaluation and" }, { "category_id": 15, "poly": [ 293.0, 1880.0, 1404.0, 1880.0, 1404.0, 1919.0, 293.0, 1919.0 ], "score": 0.99, "text": "writing; Urmish Thakker, Mike Tian-Jian Jiang, Shanya Sharma, Arnaud Stiegler,and Manan Dey" }, { "category_id": 15, "poly": [ 296.0, 1912.0, 1404.0, 1912.0, 1404.0, 1946.0, 296.0, 1946.0 ], "score": 0.976, "text": "who helped with the development of the prompting tool; M Saiful Bari, who helped for the models" }, { "category_id": 15, "poly": [ 294.0, 1944.0, 1179.0, 1944.0, 1179.0, 1978.0, 294.0, 1978.0 ], "score": 0.987, "text": "and dataset release; Teven Le Scao,who conducted the contamination analysis." }, { "category_id": 15, "poly": [ 294.0, 1039.0, 1406.0, 1039.0, 1406.0, 1077.0, 294.0, 1077.0 ], "score": 0.984, "text": "Ruiqi Zhong, Kristy Lee, Zheng Zhang,and Dan Klein. Adapting language models for zero-shot" }, { "category_id": 15, "poly": [ 323.0, 1072.0, 1404.0, 1072.0, 1404.0, 1106.0, 323.0, 1106.0 ], "score": 0.983, "text": "learning by meta-tuning on dataset and prompt collections. CoRR,abs/2104.04670, 2021. URL" }, { "category_id": 15, "poly": [ 324.0, 1103.0, 695.0, 1103.0, 695.0, 1137.0, 324.0, 1137.0 ], "score": 0.986, "text": "https://arxiv.org/abs/2104.04670." }, { "category_id": 15, "poly": [ 295.0, 228.0, 1406.0, 228.0, 1406.0, 266.0, 295.0, 266.0 ], "score": 0.982, "text": "Rowan Zellers,Ari Holtzman, Yonatan Bisk, Ali Farhadi,and Yejin Choi. Hellas wag: Can a machine" }, { "category_id": 15, "poly": [ 323.0, 262.0, 1403.0, 262.0, 1403.0, 296.0, 323.0, 296.0 ], "score": 0.988, "text": "really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for" }, { "category_id": 15, "poly": [ 325.0, 290.0, 703.0, 290.0, 703.0, 324.0, 325.0, 324.0 ], "score": 0.993, "text": "Computational Linguistics,2019." }, { "category_id": 15, "poly": [ 295.0, 739.0, 1405.0, 739.0, 1405.0, 775.0, 295.0, 775.0 ], "score": 0.984, "text": "Jieyu Zhao,Tianlu Wang,Mark Yatskar, Vicente Ordonez,and Kai-Wei Chang. Gender bias in" }, { "category_id": 15, "poly": [ 324.0, 772.0, 1402.0, 772.0, 1402.0, 804.0, 324.0, 804.0 ], "score": 0.988, "text": "coreference resolution: Evaluation and debiasing methods. In Proceedings of the 2O18 Con-" }, { "category_id": 15, "poly": [ 320.0, 803.0, 1402.0, 803.0, 1402.0, 835.0, 320.0, 835.0 ], "score": 0.992, "text": "ference of the North American Chapter of the Association for Computational Linguistics: Hu-" }, { "category_id": 15, "poly": [ 320.0, 831.0, 1405.0, 831.0, 1405.0, 867.0, 320.0, 867.0 ], "score": 0.98, "text": "man Language Technologies, Volume 2 (Short Papers), pages 15-20, New Orleans,Louisiana," }, { "category_id": 15, "poly": [ 321.0, 862.0, 1407.0, 862.0, 1407.0, 897.0, 321.0, 897.0 ], "score": 0.978, "text": "June 2018. Association for Computational Linguistics. doi: 10.18653/v1/N18-2003．URL" }, { "category_id": 15, "poly": [ 324.0, 894.0, 722.0, 894.0, 722.0, 925.0, 324.0, 925.0 ], "score": 0.999, "text": "https://aclanthology.org/N18-2003." }, { "category_id": 15, "poly": [ 295.0, 557.0, 1404.0, 557.0, 1404.0, 593.0, 295.0, 593.0 ], "score": 0.985, "text": "Xiang Zhang, Junbo Jake Zhao,and Yann LeCun. Character-level convolutional networks for text" }, { "category_id": 15, "poly": [ 325.0, 592.0, 669.0, 592.0, 669.0, 624.0, 325.0, 624.0 ], "score": 0.986, "text": "classification. In NIPS,2015b." }, { "category_id": 15, "poly": [ 295.0, 647.0, 1405.0, 647.0, 1405.0, 686.0, 295.0, 686.0 ], "score": 0.993, "text": "Yuan Zhang, Jason Baldridge,and Luheng He. PAWS: Paraphrase Adversaries from Word Scram-" }, { "category_id": 15, "poly": [ 322.0, 680.0, 691.0, 680.0, 691.0, 715.0, 322.0, 715.0 ], "score": 0.979, "text": "bling. In Proc.of NAACL,2019." }, { "category_id": 15, "poly": [ 297.0, 949.0, 1402.0, 949.0, 1402.0, 985.0, 297.0, 985.0 ], "score": 0.99, "text": "Tony Z. Zhao,Eric Wallace, Shi Feng,Dan Klein,and Sameer Singh. Calibrate before use: Improv-" }, { "category_id": 15, "poly": [ 321.0, 982.0, 918.0, 982.0, 918.0, 1017.0, 321.0, 1017.0 ], "score": 0.985, "text": "ing few-shot performance of language models, 2021." }, { "category_id": 15, "poly": [ 295.0, 469.0, 1404.0, 469.0, 1404.0, 505.0, 295.0, 505.0 ], "score": 0.996, "text": "Xiang Zhang, Junbo Zhao,and Yann LeCun. Character-level convolutional networks for text classi-" }, { "category_id": 15, "poly": [ 323.0, 501.0, 1299.0, 501.0, 1299.0, 537.0, 323.0, 537.0 ], "score": 0.995, "text": "fication. In Advances in neural information processing systems, pages 649-657,2015a." }, { "category_id": 15, "poly": [ 296.0, 349.0, 1402.0, 349.0, 1402.0, 387.0, 296.0, 387.0 ], "score": 0.996, "text": "Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh,and Benjamin Van Durme." }, { "category_id": 15, "poly": [ 322.0, 381.0, 1402.0, 381.0, 1402.0, 417.0, 322.0, 417.0 ], "score": 0.999, "text": "Record: Bridging the gap between human and machine commonsense reading comprehension." }, { "category_id": 15, "poly": [ 322.0, 411.0, 775.0, 411.0, 775.0, 444.0, 322.0, 444.0 ], "score": 0.981, "text": "arXiv preprint arXiv:1810.12885,2018." } ], "page_info": { "page_no": 16, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 1350, 1405, 1350, 1405, 1962, 298, 1962 ], "score": 0.983 }, { "category_id": 1, "poly": [ 298, 562, 1404, 562, 1404, 838, 298, 838 ], "score": 0.979 }, { "category_id": 1, "poly": [ 298, 301, 1403, 301, 1403, 546, 298, 546 ], "score": 0.977 }, { "category_id": 1, "poly": [ 298, 853, 1404, 853, 1404, 1158, 298, 1158 ], "score": 0.975 }, { "category_id": 2, "poly": [ 333, 2006, 881, 2006, 881, 2034, 333, 2034 ], "score": 0.916 }, { "category_id": 0, "poly": [ 300, 1289, 668, 1289, 668, 1321, 300, 1321 ], "score": 0.888 }, { "category_id": 0, "poly": [ 300, 1213, 632, 1213, 632, 1249, 300, 1249 ], "score": 0.886 }, { "category_id": 2, "poly": [ 300, 76, 815, 76, 815, 104, 300, 104 ], "score": 0.876 }, { "category_id": 2, "poly": [ 836, 2089, 864, 2089, 864, 2112, 836, 2112 ], "score": 0.852 }, { "category_id": 0, "poly": [ 300, 226, 1164, 226, 1164, 261, 300, 261 ], "score": 0.797 }, { "category_id": 1, "poly": [ 300, 226, 1164, 226, 1164, 261, 300, 261 ], "score": 0.108 }, { "category_id": 13, "poly": [ 540, 1778, 755, 1778, 755, 1810, 540, 1810 ], "score": 0.91, "latex": "4 1 0 / 5 4 0 \\overset { \\cdot } { \\approx } 7 5 . 9 \\%" }, { "category_id": 13, "poly": [ 646, 1808, 1024, 1808, 1024, 1839, 646, 1839 ], "score": 0.91, "latex": "4 6 . 7 \\times 2 5 \\% \\times 7 5 . 9 \\% \\approx 8 . 9 1 C O _ { 2 } \\mathrm { e }" }, { "category_id": 13, "poly": [ 633, 456, 722, 456, 722, 485, 633, 485 ], "score": 0.89, "latex": "3 3 . 6 5 \\%" }, { "category_id": 13, "poly": [ 756, 456, 845, 456, 845, 485, 756, 485 ], "score": 0.89, "latex": "5 7 . 9 3 \\%" }, { "category_id": 13, "poly": [ 399, 1656, 453, 1656, 453, 1686, 399, 1686 ], "score": 0.88, "latex": "2 5 \\%" }, { "category_id": 13, "poly": [ 409, 516, 524, 516, 524, 546, 409, 546 ], "score": 0.87, "latex": "= 6 2 . 1 5 \\%" }, { "category_id": 13, "poly": [ 296, 1902, 372, 1902, 372, 1931, 296, 1931 ], "score": 0.86, "latex": "\\mathrm { t C O _ { 2 } e }" }, { "category_id": 13, "poly": [ 901, 1718, 1059, 1718, 1059, 1750, 901, 1750 ], "score": 0.83, "latex": "\\mathrm { g C O _ { 2 } e q / k W h }" }, { "category_id": 13, "poly": [ 297, 1870, 425, 1870, 425, 1901, 297, 1901 ], "score": 0.83, "latex": "1 8 0 ~ \\mathrm { t C O _ { 2 } e }" }, { "category_id": 13, "poly": [ 476, 1505, 609, 1505, 609, 1535, 476, 1535 ], "score": 0.82, "latex": "4 6 . 7 \\ \\mathrm { t C O _ { 2 } e }" }, { "category_id": 13, "poly": [ 636, 1720, 689, 1720, 689, 1747, 636, 1747 ], "score": 0.59, "latex": "4 - a" }, { "category_id": 13, "poly": [ 298, 916, 354, 916, 354, 945, 298, 945 ], "score": 0.3, "latex": "\\tt { I } ^ { \\prime } \\thinspace \\mathrm { m }" }, { "category_id": 13, "poly": [ 805, 1505, 842, 1505, 842, 1533, 805, 1533 ], "score": 0.28, "latex": "\\mathrm { T 0 }" }, { "category_id": 15, "poly": [ 331.0, 2001.0, 884.0, 2001.0, 884.0, 2039.0, 331.0, 2039.0 ], "score": 0.989, "text": "5https://cloud.google.com/sustainability/region-carbon" }, { "category_id": 15, "poly": [ 296.0, 1289.0, 671.0, 1289.0, 671.0, 1325.0, 296.0, 1325.0 ], "score": 0.998, "text": "C.1ENVIRONMENTAL COSTS" }, { "category_id": 15, "poly": [ 294.0, 1208.0, 637.0, 1208.0, 637.0, 1257.0, 294.0, 1257.0 ], "score": 0.998, "text": "CBROADER IMPACTS" }, { "category_id": 15, "poly": [ 296.0, 73.0, 817.0, 73.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 832.0, 2085.0, 870.0, 2085.0, 870.0, 2123.0, 832.0, 2123.0 ], "score": 1.0, "text": "18" }, { "category_id": 15, "poly": [ 293.0, 227.0, 1169.0, 227.0, 1169.0, 267.0, 293.0, 267.0 ], "score": 0.988, "text": "BADDITIONAL DISCUSSION OF DIFFERENCES WITH FLAN" }, { "category_id": 15, "poly": [ 295.0, 1352.0, 1405.0, 1352.0, 1405.0, 1387.0, 295.0, 1387.0 ], "score": 0.994, "text": "Training large language models can incur substantial environmental costs (Strubell et al., 2019;" }, { "category_id": 15, "poly": [ 293.0, 1378.0, 1405.0, 1378.0, 1405.0, 1421.0, 293.0, 1421.0 ], "score": 0.974, "text": " Schwartz et al.,202O; Lacoste et al.,2019; Bender et al., 2021). These costs are due to the energy" }, { "category_id": 15, "poly": [ 295.0, 1414.0, 1407.0, 1414.0, 1407.0, 1448.0, 295.0, 1448.0 ], "score": 0.988, "text": "used to power the hardware required for training.Recently,Patterson et al. (2O21） performed a" }, { "category_id": 15, "poly": [ 292.0, 1438.0, 1406.0, 1438.0, 1406.0, 1483.0, 292.0, 1483.0 ], "score": 0.992, "text": " detailed analysis of the carbon emissions resulting from the training of various recent large language" }, { "category_id": 15, "poly": [ 293.0, 1473.0, 1405.0, 1473.0, 1405.0, 1509.0, 293.0, 1509.0 ], "score": 0.997, "text": "models. One model analyzed in that study was the largest T5 variant which was estimated to have" }, { "category_id": 15, "poly": [ 293.0, 1503.0, 475.0, 1503.0, 475.0, 1537.0, 293.0, 1537.0 ], "score": 0.991, "text": "emitted around" }, { "category_id": 15, "poly": [ 610.0, 1503.0, 804.0, 1503.0, 804.0, 1537.0, 610.0, 1537.0 ], "score": 0.951, "text": ".Since we based" }, { "category_id": 15, "poly": [ 843.0, 1503.0, 1403.0, 1503.0, 1403.0, 1537.0, 843.0, 1537.0 ], "score": 0.993, "text": "on this T5 variant and performed training on the" }, { "category_id": 15, "poly": [ 292.0, 1534.0, 1403.0, 1534.0, 1403.0, 1571.0, 292.0, 1571.0 ], "score": 0.988, "text": " same hardware (Google Cloud TPUs), we can estimate the carbon emissions produced by our study" }, { "category_id": 15, "poly": [ 293.0, 1565.0, 1406.0, 1565.0, 1406.0, 1603.0, 293.0, 1603.0 ], "score": 0.977, "text": "by simply re-scaling_the T5 estimate from Patterson et al. (2021) by the amount of training we" }, { "category_id": 15, "poly": [ 293.0, 1597.0, 1403.0, 1597.0, 1403.0, 1631.0, 293.0, 1631.0 ], "score": 0.991, "text": "performed. Specifically,T5 was pretrained for one trillion tokens; across all of our training runs" }, { "category_id": 15, "poly": [ 292.0, 1624.0, 1406.0, 1624.0, 1406.0, 1663.0, 292.0, 1663.0 ], "score": 0.987, "text": "(including preliminary test experiments not described in this paper) we trained for 250 bilion tokens," }, { "category_id": 15, "poly": [ 291.0, 1653.0, 398.0, 1653.0, 398.0, 1694.0, 291.0, 1694.0 ], "score": 0.996, "text": "or about" }, { "category_id": 15, "poly": [ 454.0, 1653.0, 1409.0, 1653.0, 1409.0, 1694.0, 454.0, 1694.0 ], "score": 0.981, "text": " as many. These training runs corresponded to about 27O total hours of training on a" }, { "category_id": 15, "poly": [ 293.0, 1684.0, 1406.0, 1684.0, 1406.0, 1723.0, 293.0, 1723.0 ], "score": 0.989, "text": "v3-512 Cloud TPU device. Further, T5 was trained in Google's Taiwan datacenter, whereas we" }, { "category_id": 15, "poly": [ 293.0, 1717.0, 635.0, 1717.0, 635.0, 1755.0, 293.0, 1755.0 ], "score": 0.98, "text": " trained in the europe-west" }, { "category_id": 15, "poly": [ 690.0, 1717.0, 900.0, 1717.0, 900.0, 1755.0, 690.0, 1755.0 ], "score": 0.984, "text": "Cloud region. The" }, { "category_id": 15, "poly": [ 1060.0, 1717.0, 1406.0, 1717.0, 1406.0, 1755.0, 1060.0, 1755.0 ], "score": 0.971, "text": "published by Google for these" }, { "category_id": 15, "poly": [ 292.0, 1745.0, 1406.0, 1745.0, 1406.0, 1786.0, 292.0, 1786.0 ], "score": 0.986, "text": " datacenters are 540 and 410 respectively,5 suggesting that our carbon emissions should further be" }, { "category_id": 15, "poly": [ 296.0, 1780.0, 539.0, 1780.0, 539.0, 1811.0, 296.0, 1811.0 ], "score": 0.966, "text": "scaled bya factor of" }, { "category_id": 15, "poly": [ 756.0, 1780.0, 1405.0, 1780.0, 1405.0, 1811.0, 756.0, 1811.0 ], "score": 0.979, "text": ".Based on the above,we estimate the total emissions for" }, { "category_id": 15, "poly": [ 295.0, 1808.0, 645.0, 1808.0, 645.0, 1843.0, 295.0, 1843.0 ], "score": 0.997, "text": "training our models to be about" }, { "category_id": 15, "poly": [ 1025.0, 1808.0, 1406.0, 1808.0, 1406.0, 1843.0, 1025.0, 1843.0 ], "score": 0.979, "text": "As a point of reference,Patterson" }, { "category_id": 15, "poly": [ 292.0, 1839.0, 1405.0, 1839.0, 1405.0, 1874.0, 292.0, 1874.0 ], "score": 0.983, "text": "et al. (2021) estimate that a roundtrip jet plane flight from San Francisco to New York emits around" }, { "category_id": 16, "poly": [ 292.0, 1866.0, 296.0, 1866.0, 296.0, 1907.0, 292.0, 1907.0 ], "score": 0.735, "text": "（20" }, { "category_id": 15, "poly": [ 426.0, 1866.0, 1408.0, 1866.0, 1408.0, 1907.0, 426.0, 1907.0 ], "score": 0.993, "text": "and Strubell et al. (2019) estimate the average per-passenger emissions to be about 1" }, { "category_id": 15, "poly": [ 373.0, 1901.0, 1406.0, 1901.0, 1406.0, 1935.0, 373.0, 1935.0 ], "score": 0.987, "text": ".Note that our experiments incurred additional emissions due to the cost of evaluation, the" }, { "category_id": 15, "poly": [ 293.0, 1929.0, 1405.0, 1929.0, 1405.0, 1968.0, 293.0, 1968.0 ], "score": 0.988, "text": "XL-sized ablation,and data preprocessing, but these costs are negligible compared to the training" }, { "category_id": 15, "poly": [ 297.0, 564.0, 1404.0, 564.0, 1404.0, 597.0, 297.0, 597.0 ], "score": 0.983, "text": "Surprisingly,Wei et al. (2O21) perform an ablation with a model of comparable size (8B parameters)" }, { "category_id": 15, "poly": [ 296.0, 594.0, 1405.0, 594.0, 1405.0, 627.0, 296.0, 627.0 ], "score": 0.984, "text": "to TO (11B parameters) and find that that performance on held-out tasks decreases after multitask" }, { "category_id": 15, "poly": [ 292.0, 622.0, 1407.0, 622.0, 1407.0, 661.0, 292.0, 661.0 ], "score": 0.979, "text": " prompted training, whereas we find that multitask prompted training improves the performance of" }, { "category_id": 15, "poly": [ 291.0, 649.0, 1406.0, 649.0, 1406.0, 692.0, 291.0, 692.0 ], "score": 0.979, "text": "models at least as small as 3B parameters (Figure 8). We identify two key diferences between" }, { "category_id": 15, "poly": [ 294.0, 683.0, 1405.0, 683.0, 1405.0, 720.0, 294.0, 720.0 ], "score": 0.981, "text": "the models that could explain this discrepancy: First, we use an encoder-decoder model that was" }, { "category_id": 15, "poly": [ 293.0, 716.0, 1405.0, 716.0, 1405.0, 750.0, 293.0, 750.0 ], "score": 0.994, "text": "pretrained with a different objective (masked language modeling) before being trained as a standard" }, { "category_id": 15, "poly": [ 293.0, 742.0, 1405.0, 742.0, 1405.0, 782.0, 293.0, 782.0 ], "score": 0.993, "text": "language model and finally fine-tuned on the multitask mixture. We note that masked language" }, { "category_id": 15, "poly": [ 294.0, 776.0, 1405.0, 776.0, 1405.0, 812.0, 294.0, 812.0 ], "score": 0.987, "text": "modeling has repeatedly been shown to be a dramatically more effective pre-training strategy (Raffel" }, { "category_id": 15, "poly": [ 294.0, 807.0, 894.0, 807.0, 894.0, 840.0, 294.0, 840.0 ], "score": 0.961, "text": "et al.,202O; Baevski et al.,2019; Devlin et al., 2019)." }, { "category_id": 15, "poly": [ 294.0, 303.0, 1405.0, 303.0, 1405.0, 336.0, 294.0, 336.0 ], "score": 0.985, "text": "Both TO and FLAN underperform GPT-3 on Winogrande and HellaSwag (Sakaguchi et al., 2019;" }, { "category_id": 15, "poly": [ 294.0, 334.0, 1403.0, 334.0, 1403.0, 367.0, 294.0, 367.0 ], "score": 0.985, "text": "Zellers et al.,2O19),for which Wei et al.(2O21) conjecture that for tasks such as coreference resolu-" }, { "category_id": 15, "poly": [ 293.0, 362.0, 1403.0, 362.0, 1403.0, 400.0, 293.0, 400.0 ], "score": 0.988, "text": " tion that can be formated as finishing an incomplete sentence,adding task instructions to prompts" }, { "category_id": 15, "poly": [ 293.0, 394.0, 1404.0, 394.0, 1404.0, 427.0, 293.0, 427.0 ], "score": 0.979, "text": " is “largely redundant\".Following this conjecture, we reevaluate these two datasets without instruc-" }, { "category_id": 15, "poly": [ 293.0, 422.0, 1404.0, 422.0, 1404.0, 459.0, 293.0, 459.0 ], "score": 0.964, "text": " tions as done by Wei et al. (2O21) and Brown et al. (202O) and find that it improves performance on" }, { "category_id": 15, "poly": [ 294.0, 456.0, 632.0, 456.0, 632.0, 489.0, 294.0, 489.0 ], "score": 0.973, "text": "HellaSwag from a median of" }, { "category_id": 15, "poly": [ 723.0, 456.0, 755.0, 456.0, 755.0, 489.0, 723.0, 489.0 ], "score": 0.97, "text": "to" }, { "category_id": 15, "poly": [ 846.0, 456.0, 1403.0, 456.0, 1403.0, 489.0, 846.0, 489.0 ], "score": 0.981, "text": ", matching the performance of FLAN.For Wino-" }, { "category_id": 15, "poly": [ 293.0, 487.0, 1405.0, 487.0, 1405.0, 518.0, 293.0, 518.0 ], "score": 0.982, "text": " grande, however, using FLAN's prompt without instructions does not make a substantial difference" }, { "category_id": 15, "poly": [ 296.0, 516.0, 408.0, 516.0, 408.0, 550.0, 296.0, 550.0 ], "score": 0.999, "text": "(accuracy" }, { "category_id": 15, "poly": [ 525.0, 516.0, 543.0, 516.0, 543.0, 550.0, 525.0, 550.0 ], "score": 0.745, "text": ")." }, { "category_id": 15, "poly": [ 296.0, 852.0, 1404.0, 852.0, 1404.0, 888.0, 296.0, 888.0 ], "score": 0.986, "text": "Second,our prompts are qualitatively more diverse in terms of their length and creativity (84)." }, { "category_id": 15, "poly": [ 296.0, 885.0, 1402.0, 885.0, 1402.0, 917.0, 296.0, 917.0 ], "score": 0.994, "text": "For example,consider one of our prompts for Quora Question Pairs (paraphrasing identification):" }, { "category_id": 15, "poly": [ 355.0, 913.0, 1345.0, 913.0, 1345.0, 951.0, 355.0, 951.0 ], "score": 0.993, "text": " an administrator on the website Quora. There are two posts," }, { "category_id": 15, "poly": [ 293.0, 945.0, 1337.0, 945.0, 1337.0, 978.0, 293.0, 978.0 ], "score": 0.99, "text": "one that asks \"questionl\" and another that asks \"question2\". I" }, { "category_id": 15, "poly": [ 294.0, 976.0, 1401.0, 976.0, 1401.0, 1012.0, 294.0, 1012.0 ], "score": 0.979, "text": "can merge questions if they are asking the same thing. Can I merge" }, { "category_id": 15, "poly": [ 293.0, 1006.0, 1405.0, 1006.0, 1405.0, 1038.0, 293.0, 1038.0 ], "score": 0.997, "text": "these two questions? We hypothesize that this diversity could have concrete effects.For" }, { "category_id": 15, "poly": [ 294.0, 1036.0, 1406.0, 1036.0, 1406.0, 1071.0, 294.0, 1071.0 ], "score": 0.982, "text": "example, it could explain why Wei et al. (2O21） present ablation results where increasing the" }, { "category_id": 15, "poly": [ 296.0, 1069.0, 1404.0, 1069.0, 1404.0, 1100.0, 296.0, 1100.0 ], "score": 0.998, "text": "number of prompts has a negligible impact on performance whereas we observe an improvement" }, { "category_id": 15, "poly": [ 294.0, 1097.0, 1405.0, 1097.0, 1405.0, 1132.0, 294.0, 1132.0 ], "score": 0.99, "text": "when adding more prompts (86.2). We leave a full investigation on the impact of these differences" }, { "category_id": 15, "poly": [ 294.0, 1126.0, 469.0, 1126.0, 469.0, 1160.0, 294.0, 1160.0 ], "score": 0.999, "text": "to future work." }, { "category_id": 15, "poly": [ 293.0, 227.0, 1169.0, 227.0, 1169.0, 267.0, 293.0, 267.0 ], "score": 0.988, "text": "BADDITIONAL DISCUSSION OF DIFFERENCES WITH FLAN" } ], "page_info": { "page_no": 17, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 837, 1403, 837, 1403, 1082, 298, 1082 ], "score": 0.981 }, { "category_id": 1, "poly": [ 297, 607, 1404, 607, 1404, 823, 297, 823 ], "score": 0.979 }, { "category_id": 1, "poly": [ 298, 1515, 1403, 1515, 1403, 1670, 298, 1670 ], "score": 0.976 }, { "category_id": 1, "poly": [ 301, 1098, 1404, 1098, 1404, 1191, 301, 1191 ], "score": 0.969 }, { "category_id": 5, "poly": [ 492, 1804, 1209, 1804, 1209, 1974, 492, 1974 ], "score": 0.966, "html": "
InputPrediction
Is the earth flat?yes
Do vaccines cause autism?yes
Complete this sentence: This man works as aArchitect
Complete this sentence: This woman works as aNanny
" }, { "category_id": 1, "poly": [ 301, 1685, 1398, 1685, 1398, 1778, 301, 1778 ], "score": 0.966 }, { "category_id": 5, "poly": [ 299, 317, 1408, 317, 1408, 450, 299, 450 ], "score": 0.963, "html": "
ModelHardwareHoursGridgCO2eq/kWhEstimated tCO2e
TO (single run)v3-51227europe-west4-a4100.9
All experiments in this paperv3-512270europe-west4-a4108.9
T5-11B (single run)v3-1024528Taiwan54046.7
" }, { "category_id": 1, "poly": [ 372, 1216, 1405, 1216, 1405, 1365, 372, 1365 ], "score": 0.937 }, { "category_id": 0, "poly": [ 299, 1458, 614, 1458, 614, 1491, 299, 1491 ], "score": 0.919 }, { "category_id": 2, "poly": [ 299, 75, 815, 75, 815, 105, 299, 105 ], "score": 0.902 }, { "category_id": 2, "poly": [ 836, 2088, 865, 2088, 865, 2112, 836, 2112 ], "score": 0.867 }, { "category_id": 6, "poly": [ 548, 470, 1151, 470, 1151, 502, 548, 502 ], "score": 0.819 }, { "category_id": 6, "poly": [ 288, 229, 1404, 229, 1404, 295, 288, 295 ], "score": 0.786 }, { "category_id": 6, "poly": [ 561, 1997, 1136, 1997, 1136, 2029, 561, 2029 ], "score": 0.761 }, { "category_id": 0, "poly": [ 299, 550, 1192, 550, 1192, 583, 299, 583 ], "score": 0.501 }, { "category_id": 13, "poly": [ 1063, 1997, 1128, 1997, 1128, 2026, 1063, 2026 ], "score": 0.85, "latex": "\\mathrm { T 0 + + }" }, { "category_id": 13, "poly": [ 1185, 1717, 1253, 1717, 1253, 1745, 1185, 1745 ], "score": 0.83, "latex": "\\mathrm { T 0 + + }" }, { "category_id": 13, "poly": [ 950, 869, 1044, 869, 1044, 899, 950, 899 ], "score": 0.71, "latex": "_ { \\mathrm { T 5 + L M } }" }, { "category_id": 15, "poly": [ 295.0, 1456.0, 618.0, 1456.0, 618.0, 1495.0, 295.0, 1495.0 ], "score": 0.997, "text": "C.3BIAS AND FAIRNESS" }, { "category_id": 15, "poly": [ 295.0, 73.0, 817.0, 73.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.99, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 832.0, 2085.0, 870.0, 2085.0, 870.0, 2124.0, 832.0, 2124.0 ], "score": 1.0, "text": "19" }, { "category_id": 15, "poly": [ 545.0, 468.0, 1151.0, 468.0, 1151.0, 504.0, 545.0, 504.0 ], "score": 0.989, "text": "Table 1: Carbon emissions information for TO and T5." }, { "category_id": 15, "poly": [ 294.0, 230.0, 1403.0, 230.0, 1403.0, 266.0, 294.0, 266.0 ], "score": 0.996, "text": "runs for the main TO model. Moreover, most of the evaluations and data preprocessing ran on the" }, { "category_id": 15, "poly": [ 294.0, 259.0, 1161.0, 259.0, 1161.0, 298.0, 294.0, 298.0 ], "score": 0.998, "text": "French Jean-Zay cluster whose electricity mostly comes from nuclear energy." }, { "category_id": 15, "poly": [ 561.0, 1995.0, 1062.0, 1995.0, 1062.0, 2032.0, 561.0, 2032.0 ], "score": 0.99, "text": "Table 2: A few problematic predictions from" }, { "category_id": 16, "poly": [ 1129.0, 1995.0, 1138.0, 1995.0, 1138.0, 2032.0, 1129.0, 2032.0 ], "score": 0.0, "text": "" }, { "category_id": 15, "poly": [ 295.0, 549.0, 1198.0, 549.0, 1198.0, 587.0, 295.0, 587.0 ], "score": 0.992, "text": "C.2RISKS IN DEVELOPING AND RELEASING LARGE LANGUAGE MODELS" }, { "category_id": 15, "poly": [ 294.0, 836.0, 1404.0, 836.0, 1404.0, 873.0, 294.0, 873.0 ], "score": 0.984, "text": "This work is built exclusively on publicly available datasets from the Hugging Face datasets li-" }, { "category_id": 15, "poly": [ 293.0, 870.0, 949.0, 870.0, 949.0, 901.0, 293.0, 901.0 ], "score": 0.96, "text": "brary (Lhoest et al.,2O21) and a publicly available model," }, { "category_id": 15, "poly": [ 1045.0, 870.0, 1403.0, 870.0, 1403.0, 901.0, 1045.0, 901.0 ], "score": 0.944, "text": "(Lester et al., 2021). The impli-" }, { "category_id": 15, "poly": [ 294.0, 900.0, 1405.0, 900.0, 1405.0, 933.0, 294.0, 933.0 ], "score": 0.986, "text": "cations of releasing large language models have been extensively discussed in Bender et al. (2021);" }, { "category_id": 15, "poly": [ 293.0, 928.0, 1405.0, 928.0, 1405.0, 964.0, 293.0, 964.0 ], "score": 0.977, "text": "Bommasani et al. (2O21); Solaiman et al. (2019) among others. We expect replicating our work to be" }, { "category_id": 15, "poly": [ 296.0, 959.0, 1405.0, 959.0, 1405.0, 993.0, 296.0, 993.0 ], "score": 0.983, "text": "within the capabilities of dozens of organizations worldwide,the main barrer being financial con-" }, { "category_id": 15, "poly": [ 293.0, 989.0, 1405.0, 989.0, 1405.0, 1025.0, 293.0, 1025.0 ], "score": 0.988, "text": " straints. As such, we believe that the additional potential risks and harms produced by releasing our" }, { "category_id": 15, "poly": [ 293.0, 1019.0, 1405.0, 1019.0, 1405.0, 1055.0, 293.0, 1055.0 ], "score": 0.988, "text": "model are limited,and not releasing it would primarily impact less funded research organizations" }, { "category_id": 15, "poly": [ 296.0, 1052.0, 480.0, 1052.0, 480.0, 1083.0, 296.0, 1083.0 ], "score": 1.0, "text": "and individuals." }, { "category_id": 15, "poly": [ 295.0, 606.0, 1404.0, 606.0, 1404.0, 643.0, 295.0, 643.0 ], "score": 0.991, "text": "The focus of this paper is an empirical exploration of multitask prompt training and how it improves" }, { "category_id": 15, "poly": [ 295.0, 640.0, 1405.0, 640.0, 1405.0, 674.0, 295.0, 674.0 ], "score": 0.986, "text": " zero-shot performance on multiple tasks. We transformed datasets by writing multiple prompts for" }, { "category_id": 15, "poly": [ 292.0, 664.0, 1406.0, 664.0, 1406.0, 708.0, 292.0, 708.0 ], "score": 0.998, "text": " each of the datasets, fine-tuned pretrained models on the transformed examples and observed strong" }, { "category_id": 15, "poly": [ 292.0, 700.0, 1404.0, 700.0, 1404.0, 732.0, 292.0, 732.0 ], "score": 0.992, "text": "zero-shot capabilities on multiple tasks.We note that the zero-shot performance of our model is still" }, { "category_id": 15, "poly": [ 292.0, 727.0, 1405.0, 727.0, 1405.0, 768.0, 292.0, 768.0 ], "score": 0.988, "text": " significantly behind models that are fine-tuned on the given task in a “traditional” transfer-learning" }, { "category_id": 15, "poly": [ 292.0, 761.0, 1405.0, 761.0, 1405.0, 793.0, 292.0, 793.0 ], "score": 0.981, "text": "setup.This highlights how much research is still needed in this area,and we believe this work and" }, { "category_id": 15, "poly": [ 293.0, 792.0, 1120.0, 792.0, 1120.0, 825.0, 293.0, 825.0 ], "score": 0.988, "text": "the resources developed as part of this work are central to future research." }, { "category_id": 15, "poly": [ 296.0, 1517.0, 1404.0, 1517.0, 1404.0, 1550.0, 296.0, 1550.0 ], "score": 0.986, "text": "Some of the datasets available in the Hugging Face library contain potentially harmful content. We" }, { "category_id": 15, "poly": [ 296.0, 1547.0, 1404.0, 1547.0, 1404.0, 1581.0, 296.0, 1581.0 ], "score": 0.988, "text": "deliberately excluded these datasets from the training mixture to avoid training the model to generate" }, { "category_id": 15, "poly": [ 293.0, 1577.0, 1405.0, 1577.0, 1405.0, 1611.0, 293.0, 1611.0 ], "score": 0.982, "text": " such outputs.In particular, we excluded datasets that are constructed from forums and social media" }, { "category_id": 15, "poly": [ 293.0, 1606.0, 1405.0, 1606.0, 1405.0, 1642.0, 293.0, 1642.0 ], "score": 0.977, "text": "content such as Sentiment140 (Go et al.,2Oo9) which was based on Twitter content or Hate Speech" }, { "category_id": 15, "poly": [ 296.0, 1635.0, 1228.0, 1635.0, 1228.0, 1675.0, 296.0, 1675.0 ], "score": 0.975, "text": "18 (de Gibert et al.,2018) which was built on data from a white supremacist forum." }, { "category_id": 15, "poly": [ 295.0, 1097.0, 1405.0, 1097.0, 1405.0, 1135.0, 295.0, 1135.0 ], "score": 0.986, "text": "Moreover, we favor openness, transparency, and reproducibility. Releasing the dataset, models and" }, { "category_id": 15, "poly": [ 294.0, 1126.0, 1406.0, 1126.0, 1406.0, 1165.0, 294.0, 1165.0 ], "score": 0.987, "text": "tools developed as part of this work are key elements that will facilitate the reproduction of our" }, { "category_id": 15, "poly": [ 296.0, 1161.0, 1023.0, 1161.0, 1023.0, 1192.0, 296.0, 1192.0 ], "score": 0.983, "text": "results and future works.As such,our resources are available at :" }, { "category_id": 15, "poly": [ 297.0, 1687.0, 1402.0, 1687.0, 1402.0, 1717.0, 297.0, 1717.0 ], "score": 0.997, "text": "Even if we took deliberate decisions to exclude datasets with potentially harmful content from the" }, { "category_id": 15, "poly": [ 296.0, 1713.0, 1184.0, 1713.0, 1184.0, 1751.0, 296.0, 1751.0 ], "score": 0.994, "text": "fine-tuning,the models trained are not bias-free.Based on a few experiments," }, { "category_id": 15, "poly": [ 1254.0, 1713.0, 1403.0, 1713.0, 1403.0, 1751.0, 1254.0, 1751.0 ], "score": 1.0, "text": "can generate" }, { "category_id": 15, "poly": [ 295.0, 1747.0, 962.0, 1747.0, 962.0, 1780.0, 295.0, 1780.0 ], "score": 0.979, "text": "answers that could be categorized as conspiracist or biased:" }, { "category_id": 15, "poly": [ 369.0, 1216.0, 1257.0, 1216.0, 1257.0, 1254.0, 369.0, 1254.0 ], "score": 0.994, "text": "· Collection of prompts: http://github.com/bigscience-workshop/promptsource" }, { "category_id": 15, "poly": [ 370.0, 1257.0, 1110.0, 1257.0, 1110.0, 1294.0, 370.0, 1294.0 ], "score": 0.994, "text": "· Trained models: https://github.com/bigscience-workshop/t-zero" }, { "category_id": 15, "poly": [ 369.0, 1298.0, 1404.0, 1298.0, 1404.0, 1335.0, 369.0, 1335.0 ], "score": 0.998, "text": "·Materialized prompted data used to train the models: https://huggingface.co/datasets/" }, { "category_id": 15, "poly": [ 393.0, 1331.0, 559.0, 1331.0, 559.0, 1366.0, 393.0, 1366.0 ], "score": 1.0, "text": "bigscience/P3" } ], "page_info": { "page_no": 18, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 342, 1436, 1357, 1436, 1357, 1949, 342, 1949 ], "score": 0.984, "html": "
ModelSubsetAverage (Acc.)Median (Acc.)
ProAntiPro - AntiProAntiPro - Anti
T0Type 168.061.96.071.761.99.8
Type 279.376.42.879.375.04.3
TO+Type 166.657.29.471.562.68.8
Type 277.773.44.386.181.34.8
T0++Type 163.855.97.972.763.49.3
Type 266.863.03.979.374.05.3
T0 (p=1)Type 173.760.513.279.360.618.7
Type 277.769.68.080.869.711.1
T0 (original task only)Type 178.167.710.481.867.214.6
Type 285.282.32.989.685.44.3
T0 (3B)Type 182.370.112.283.662.920.7
Type 283.876.57.385.975.010.9
" }, { "category_id": 1, "poly": [ 298, 337, 1403, 337, 1403, 615, 298, 615 ], "score": 0.983 }, { "category_id": 1, "poly": [ 298, 1116, 1404, 1116, 1404, 1395, 298, 1395 ], "score": 0.978 }, { "category_id": 5, "poly": [ 537, 654, 1162, 654, 1162, 992, 537, 992 ], "score": 0.971, "html": "
DatasetModelMean (Acc.)Median (Acc.)
CrowS-PairsTO59.283.8
TO+57.683.8
TO++62.764.4
T0 (p=1)57.669.5
T0 (3B)56.982.6
WinoGenderTO84.284.3
TO+80.180.6
TO++89.290.0
T0 (p=1)81.684.6
T0 (3B)69.769.4
" }, { "category_id": 1, "poly": [ 299, 229, 1400, 229, 1400, 323, 299, 323 ], "score": 0.966 }, { "category_id": 2, "poly": [ 300, 76, 815, 76, 815, 104, 300, 104 ], "score": 0.901 }, { "category_id": 6, "poly": [ 564, 1970, 1134, 1970, 1134, 2003, 564, 2003 ], "score": 0.888 }, { "category_id": 2, "poly": [ 835, 2088, 865, 2088, 865, 2113, 835, 2113 ], "score": 0.873 }, { "category_id": 6, "poly": [ 297, 1014, 1400, 1014, 1400, 1075, 297, 1075 ], "score": 0.814 }, { "category_id": 6, "poly": [ 298, 1013, 1399, 1013, 1399, 1075, 298, 1075 ], "score": 0.175 }, { "category_id": 13, "poly": [ 1012, 371, 1030, 371, 1030, 401, 1012, 401 ], "score": 0.38, "latex": "\\mathbf { g }" }, { "category_id": 13, "poly": [ 402, 1749, 456, 1749, 456, 1779, 402, 1779 ], "score": 0.26, "latex": "{ \\mathrm { ( p { = } 1 } } )" }, { "category_id": 15, "poly": [ 296.0, 73.0, 817.0, 73.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 564.0, 1969.0, 1134.0, 1969.0, 1134.0, 2004.0, 564.0, 2004.0 ], "score": 0.951, "text": "Table 4: Accuracies on WinoBias coreference task." }, { "category_id": 15, "poly": [ 829.0, 2083.0, 871.0, 2083.0, 871.0, 2123.0, 829.0, 2123.0 ], "score": 1.0, "text": "20" }, { "category_id": 15, "poly": [ 295.0, 1013.0, 1402.0, 1013.0, 1402.0, 1048.0, 295.0, 1048.0 ], "score": 0.99, "text": "Table 3: Average and median accuracies on CrowS-Pairs and WinoGender reformulated as classifi-" }, { "category_id": 15, "poly": [ 292.0, 1041.0, 440.0, 1041.0, 440.0, 1077.0, 292.0, 1077.0 ], "score": 1.0, "text": "cation tasks." }, { "category_id": 15, "poly": [ 294.0, 1012.0, 1403.0, 1012.0, 1403.0, 1048.0, 294.0, 1048.0 ], "score": 0.992, "text": "Table 3: Average and median accuracies on CrowS-Pairs and WinoGender reformulated as classifi-" }, { "category_id": 15, "poly": [ 296.0, 1044.0, 441.0, 1044.0, 441.0, 1077.0, 296.0, 1077.0 ], "score": 0.998, "text": "cation tasks." }, { "category_id": 15, "poly": [ 294.0, 336.0, 1405.0, 336.0, 1405.0, 374.0, 294.0, 374.0 ], "score": 0.977, "text": "To measure the ability of our model to recognize gender biases, we evaluate our models using the" }, { "category_id": 15, "poly": [ 296.0, 367.0, 1011.0, 367.0, 1011.0, 404.0, 296.0, 404.0 ], "score": 0.966, "text": "WinoGender Schemas (Rudinger et al., 2018) (also called AX-" }, { "category_id": 15, "poly": [ 1031.0, 367.0, 1405.0, 367.0, 1405.0, 404.0, 1031.0, 404.0 ], "score": 0.984, "text": "under SuperGLUE) and CrowS-" }, { "category_id": 15, "poly": [ 293.0, 397.0, 1404.0, 397.0, 1404.0, 437.0, 293.0, 437.0 ], "score": 0.983, "text": "Pairs (Nangia et al., 2O2O). WinoGender Schemas are minimal pairs of sentences that differ only by" }, { "category_id": 15, "poly": [ 293.0, 427.0, 1407.0, 427.0, 1407.0, 466.0, 293.0, 466.0 ], "score": 0.978, "text": " the gender of one pronoun in the sentence,designed to test for the presence of gender bias. We use" }, { "category_id": 15, "poly": [ 294.0, 460.0, 1407.0, 460.0, 1407.0, 497.0, 294.0, 497.0 ], "score": 0.989, "text": "the version from Poliak et al. (2O18) that casts WinoGender as a textual entailment task and report" }, { "category_id": 15, "poly": [ 294.0, 490.0, 1405.0, 490.0, 1405.0, 527.0, 294.0, 527.0 ], "score": 0.992, "text": "accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical" }, { "category_id": 15, "poly": [ 293.0, 518.0, 1405.0, 518.0, 1405.0, 559.0, 293.0, 559.0 ], "score": 0.997, "text": " biases present in the masked language models using minimal pairs of sentences. We re-formulate the" }, { "category_id": 15, "poly": [ 293.0, 547.0, 1405.0, 547.0, 1405.0, 592.0, 293.0, 592.0 ], "score": 0.985, "text": " task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy." }, { "category_id": 15, "poly": [ 293.0, 580.0, 933.0, 580.0, 933.0, 619.0, 293.0, 619.0 ], "score": 0.989, "text": "For each dataset, we evaluate between 5 and 10 prompts." }, { "category_id": 15, "poly": [ 296.0, 1116.0, 1404.0, 1116.0, 1404.0, 1152.0, 296.0, 1152.0 ], "score": 0.988, "text": "To measure the extent to which our model reproduces gender biases, we evaluate our models us-" }, { "category_id": 15, "poly": [ 293.0, 1146.0, 1404.0, 1146.0, 1404.0, 1184.0, 293.0, 1184.0 ], "score": 0.972, "text": "ing the WinoBias Schemas (Zhao et al., 2018). WinoBias Schemas are pronoun coreference res-" }, { "category_id": 15, "poly": [ 293.0, 1178.0, 1406.0, 1178.0, 1406.0, 1215.0, 293.0, 1215.0 ], "score": 0.993, "text": "olution tasks that have the potential to be influenced by gender bias.WinoBias Schemas has two" }, { "category_id": 15, "poly": [ 294.0, 1209.0, 1404.0, 1209.0, 1404.0, 1246.0, 294.0, 1246.0 ], "score": 0.984, "text": " schemas (typel and type2) which are partitioned into pro-stereotype and anti-stereotype subsets." }, { "category_id": 15, "poly": [ 293.0, 1238.0, 1406.0, 1238.0, 1406.0, 1279.0, 293.0, 1279.0 ], "score": 0.991, "text": "A \"pro-stereotype” example is one where the correct answer conforms to stereotypes, while an" }, { "category_id": 15, "poly": [ 292.0, 1266.0, 1405.0, 1266.0, 1405.0, 1308.0, 292.0, 1308.0 ], "score": 0.989, "text": "\"anti-stereotype” example is one where it opposes stereotypes. Al examples have an unambigu-" }, { "category_id": 15, "poly": [ 296.0, 1300.0, 1402.0, 1300.0, 1402.0, 1333.0, 296.0, 1333.0 ], "score": 0.98, "text": "ously correct answer,and so the difference in scores between the \"pro-”and \"anti-” subset measures" }, { "category_id": 15, "poly": [ 295.0, 1329.0, 1405.0, 1329.0, 1405.0, 1367.0, 295.0, 1367.0 ], "score": 0.995, "text": "the extent to which stereotypes can lead the model astray.We report accuracies by considering a" }, { "category_id": 15, "poly": [ 293.0, 1359.0, 1400.0, 1359.0, 1400.0, 1400.0, 293.0, 1400.0 ], "score": 0.983, "text": " prediction correct if the target noun is present in the model's prediction. We evaluate on 6 prompts." }, { "category_id": 15, "poly": [ 293.0, 226.0, 1405.0, 226.0, 1405.0, 268.0, 293.0, 268.0 ], "score": 0.996, "text": "Language models can reproduce undesirable social biases represented in the large corpus they are" }, { "category_id": 15, "poly": [ 295.0, 261.0, 1405.0, 261.0, 1405.0, 295.0, 295.0, 295.0 ], "score": 0.985, "text": "pre-trained on. We evaluate our models in two ways: first, in their ability to recognize or label gender" }, { "category_id": 15, "poly": [ 294.0, 288.0, 1075.0, 288.0, 1075.0, 328.0, 294.0, 328.0 ], "score": 0.991, "text": "biases and second, in the extent to which they reproduce those biases." } ], "page_info": { "page_no": 19, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 481, 1404, 481, 1404, 877, 298, 877 ], "score": 0.983 }, { "category_id": 1, "poly": [ 298, 1414, 1404, 1414, 1404, 1811, 298, 1811 ], "score": 0.982 }, { "category_id": 1, "poly": [ 298, 894, 1403, 894, 1403, 1169, 298, 1169 ], "score": 0.981 }, { "category_id": 1, "poly": [ 299, 1184, 1403, 1184, 1403, 1399, 299, 1399 ], "score": 0.98 }, { "category_id": 1, "poly": [ 299, 343, 1403, 343, 1403, 465, 299, 465 ], "score": 0.977 }, { "category_id": 2, "poly": [ 333, 1974, 661, 1974, 661, 2034, 333, 2034 ], "score": 0.959 }, { "category_id": 2, "poly": [ 299, 75, 816, 75, 816, 104, 299, 104 ], "score": 0.862 }, { "category_id": 2, "poly": [ 835, 2088, 863, 2088, 863, 2112, 835, 2112 ], "score": 0.834 }, { "category_id": 0, "poly": [ 298, 226, 935, 226, 935, 261, 298, 261 ], "score": 0.774 }, { "category_id": 1, "poly": [ 298, 226, 935, 226, 935, 261, 298, 261 ], "score": 0.129 }, { "category_id": 13, "poly": [ 298, 1277, 487, 1277, 487, 1310, 298, 1310 ], "score": 0.91, "latex": "\\{ \\{ { \\mathrm { e n t a i l e d } } \\} \\}" }, { "category_id": 13, "poly": [ 407, 1245, 582, 1245, 582, 1279, 407, 1279 ], "score": 0.77, "latex": "\\{ \\{ \\mathsf { p r e m i s e } \\} \\}" }, { "category_id": 15, "poly": [ 330.0, 1969.0, 539.0, 1969.0, 539.0, 2007.0, 330.0, 2007.0 ], "score": 0.95, "text": "6https://treamlit.io/" }, { "category_id": 15, "poly": [ 331.0, 2001.0, 663.0, 2001.0, 663.0, 2039.0, 331.0, 2039.0 ], "score": 0.983, "text": "7ttps://jinja.palletsprojects.com" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.992, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2084.0, 869.0, 2084.0, 869.0, 2125.0, 829.0, 2125.0 ], "score": 1.0, "text": "21" }, { "category_id": 15, "poly": [ 294.0, 226.0, 940.0, 226.0, 940.0, 268.0, 294.0, 268.0 ], "score": 0.987, "text": "DANNOTATION SYSTEM - PROMPTSOURCE" }, { "category_id": 15, "poly": [ 296.0, 480.0, 1405.0, 480.0, 1405.0, 515.0, 296.0, 515.0 ], "score": 0.99, "text": "Testing iterations of the interface on pilot template-writing tasks,we converged on three views for" }, { "category_id": 15, "poly": [ 292.0, 507.0, 1406.0, 507.0, 1406.0, 551.0, 292.0, 551.0 ], "score": 0.976, "text": " the interface. First, a“helicopter” view allows users to see what datasets are available for writing" }, { "category_id": 15, "poly": [ 294.0, 542.0, 1405.0, 542.0, 1405.0, 578.0, 294.0, 578.0 ], "score": 0.984, "text": "templates and how many are writen for each, to prioritize user attention. Second,a“sourcing\" view" }, { "category_id": 15, "poly": [ 294.0, 573.0, 1405.0, 573.0, 1405.0, 609.0, 294.0, 609.0 ], "score": 0.992, "text": " allows users to select a dataset to prompt, browse examples from that dataset in the form of Python" }, { "category_id": 15, "poly": [ 293.0, 601.0, 1406.0, 601.0, 1406.0, 639.0, 293.0, 639.0 ], "score": 0.993, "text": "dictionaries provided by the Hugging Face datasets library,and enter a template for that dataset. As" }, { "category_id": 15, "poly": [ 293.0, 632.0, 1406.0, 632.0, 1406.0, 670.0, 293.0, 670.0 ], "score": 0.984, "text": " the user writes their template, every time they save it,the output of the template applied to the current" }, { "category_id": 15, "poly": [ 293.0, 665.0, 1406.0, 665.0, 1406.0, 700.0, 293.0, 700.0 ], "score": 0.979, "text": "example is displayed next to the editor. We also collect metadata like a name for the template, and" }, { "category_id": 15, "poly": [ 293.0, 695.0, 1406.0, 695.0, 1406.0, 731.0, 293.0, 731.0 ], "score": 0.981, "text": "a reference for any bibliographic information or rationale for the template.Third, in the “prompted" }, { "category_id": 15, "poly": [ 293.0, 724.0, 1408.0, 724.0, 1408.0, 764.0, 293.0, 764.0 ], "score": 0.989, "text": "dataset” view, users can select templates and browse the prompts generated by them. The original" }, { "category_id": 15, "poly": [ 292.0, 753.0, 1406.0, 753.0, 1406.0, 791.0, 292.0, 791.0 ], "score": 0.98, "text": " example (a Python dictionary) is viewed side-by-side with the resulting prompt, with the substituted" }, { "category_id": 15, "poly": [ 294.0, 786.0, 1404.0, 786.0, 1404.0, 822.0, 294.0, 822.0 ], "score": 0.989, "text": "texthighlighted to distinguish from text hard-coded in the template. Users can quickly scroll through" }, { "category_id": 15, "poly": [ 292.0, 815.0, 1406.0, 815.0, 1406.0, 855.0, 292.0, 855.0 ], "score": 0.985, "text": " many examples, verify the behavior of their template,and return to the sourcing view if changes are" }, { "category_id": 15, "poly": [ 292.0, 847.0, 389.0, 847.0, 389.0, 879.0, 292.0, 879.0 ], "score": 1.0, "text": "needed." }, { "category_id": 15, "poly": [ 296.0, 1416.0, 1401.0, 1416.0, 1401.0, 1448.0, 296.0, 1448.0 ], "score": 0.99, "text": "During the development of our tool (which we called prompt source), we found that a few id-" }, { "category_id": 15, "poly": [ 294.0, 1447.0, 1404.0, 1447.0, 1404.0, 1479.0, 294.0, 1479.0 ], "score": 0.976, "text": "ioms were particularly useful. First, not all templates are applicable to all examples in a dataset." }, { "category_id": 15, "poly": [ 296.0, 1475.0, 1405.0, 1475.0, 1405.0, 1512.0, 296.0, 1512.0 ], "score": 0.991, "text": "Users can wrap templates in Jinja's built-in conditional statements,and any example that results in" }, { "category_id": 15, "poly": [ 293.0, 1505.0, 1405.0, 1505.0, 1405.0, 1541.0, 293.0, 1541.0 ], "score": 0.986, "text": " an empty prompt is simply skipped. Second, many examples can be used to make multiple training" }, { "category_id": 15, "poly": [ 293.0, 1536.0, 1406.0, 1536.0, 1406.0, 1571.0, 293.0, 1571.0 ], "score": 0.982, "text": " prompts,such as a question that has multiple valid answers. We therefore added a choice function" }, { "category_id": 15, "poly": [ 294.0, 1566.0, 1405.0, 1566.0, 1405.0, 1602.0, 294.0, 1602.0 ], "score": 0.981, "text": "that selects an element from a list in a way that can be controled during dataset generation, such as" }, { "category_id": 15, "poly": [ 293.0, 1597.0, 1405.0, 1597.0, 1405.0, 1633.0, 293.0, 1633.0 ], "score": 0.992, "text": " picking a random element using a seeded random number generator or generating different prompts" }, { "category_id": 15, "poly": [ 293.0, 1623.0, 1404.0, 1623.0, 1404.0, 1666.0, 293.0, 1666.0 ], "score": 0.988, "text": "for each combination of elements in the template. Third, many tasks such as classification and binary" }, { "category_id": 15, "poly": [ 293.0, 1659.0, 1404.0, 1659.0, 1404.0, 1694.0, 293.0, 1694.0 ], "score": 0.993, "text": "question answering have a small set of possible valid completions,and it is common to make pre-" }, { "category_id": 15, "poly": [ 293.0, 1686.0, 1405.0, 1686.0, 1405.0, 1724.0, 293.0, 1724.0 ], "score": 0.992, "text": "dictions for these tasks by scoring only the valid completions and returning the highest one (Brown" }, { "category_id": 15, "poly": [ 294.0, 1721.0, 1406.0, 1721.0, 1406.0, 1754.0, 294.0, 1754.0 ], "score": 0.983, "text": "et al.,2O2O). Users therefore can list the valid completions in a separate field and access them as a" }, { "category_id": 15, "poly": [ 291.0, 1747.0, 1405.0, 1747.0, 1405.0, 1786.0, 291.0, 1786.0 ], "score": 0.996, "text": "list in their templates. These completions are then explicitly available when evaluating predictions" }, { "category_id": 15, "poly": [ 294.0, 1781.0, 503.0, 1781.0, 503.0, 1814.0, 294.0, 1814.0 ], "score": 0.998, "text": "for these prompts." }, { "category_id": 15, "poly": [ 294.0, 893.0, 1405.0, 893.0, 1405.0, 929.0, 294.0, 929.0 ], "score": 0.991, "text": "A key design decision is the format for templates.We experimented with multiple formats and found" }, { "category_id": 15, "poly": [ 293.0, 922.0, 1403.0, 922.0, 1403.0, 960.0, 293.0, 960.0 ], "score": 0.991, "text": " that they exhibited a tradeoff between expressivity and explicit structure. On one side,a maximally" }, { "category_id": 15, "poly": [ 294.0, 957.0, 1404.0, 957.0, 1404.0, 990.0, 294.0, 990.0 ], "score": 0.991, "text": "expressive format such as pure Python code would let users write complex programs to manipu-" }, { "category_id": 15, "poly": [ 291.0, 983.0, 1407.0, 983.0, 1407.0, 1022.0, 291.0, 1022.0 ], "score": 0.993, "text": "late the semi-structured examples into prompts. However, analyzing these programs to understand" }, { "category_id": 15, "poly": [ 294.0, 1016.0, 1405.0, 1016.0, 1405.0, 1052.0, 294.0, 1052.0 ], "score": 0.98, "text": "how the prompts are created becomes difficult. This diffculty limits downstream manipulation and" }, { "category_id": 15, "poly": [ 296.0, 1047.0, 1400.0, 1047.0, 1400.0, 1080.0, 296.0, 1080.0 ], "score": 0.99, "text": "analysis of the templates, such as automatic template augmentation. On the other side,a maximally" }, { "category_id": 15, "poly": [ 296.0, 1078.0, 1404.0, 1078.0, 1404.0, 1111.0, 296.0, 1111.0 ], "score": 0.991, "text": "structured format such as rule-based generation limits the kinds of templates that users can create." }, { "category_id": 15, "poly": [ 293.0, 1103.0, 1407.0, 1103.0, 1407.0, 1146.0, 293.0, 1146.0 ], "score": 0.994, "text": "We found it infeasible to enumerate types of rules sufficient for the wide range of tasks and data" }, { "category_id": 15, "poly": [ 295.0, 1137.0, 748.0, 1137.0, 748.0, 1172.0, 295.0, 1172.0 ], "score": 0.987, "text": "formats for which we wanted templates." }, { "category_id": 15, "poly": [ 295.0, 1182.0, 1403.0, 1182.0, 1403.0, 1220.0, 295.0, 1220.0 ], "score": 0.986, "text": "We therefore settled on a middle ground between the two: the Jinja templating engine7 originally" }, { "category_id": 15, "poly": [ 294.0, 1214.0, 1405.0, 1214.0, 1405.0, 1252.0, 294.0, 1252.0 ], "score": 0.992, "text": "designed for producing web markup. Users write templates as prompts with placeholders, such" }, { "category_id": 15, "poly": [ 292.0, 1246.0, 406.0, 1246.0, 406.0, 1280.0, 292.0, 1280.0 ], "score": 0.999, "text": "asIf" }, { "category_id": 15, "poly": [ 583.0, 1246.0, 1402.0, 1246.0, 1402.0, 1280.0, 583.0, 1280.0 ], "score": 0.958, "text": " is true，is it also true that {{hypothesis}}? |ll" }, { "category_id": 15, "poly": [ 488.0, 1276.0, 1405.0, 1276.0, 1405.0, 1314.0, 488.0, 1314.0 ], "score": 0.972, "text": ". The separator丨|| denotes the break between the conditioning text and the" }, { "category_id": 15, "poly": [ 295.0, 1306.0, 1405.0, 1306.0, 1405.0, 1341.0, 295.0, 1341.0 ], "score": 0.994, "text": "desired completion.Placeholders refer to fields in the underlying example dictionary. Users also" }, { "category_id": 15, "poly": [ 293.0, 1337.0, 1405.0, 1337.0, 1405.0, 1371.0, 293.0, 1371.0 ], "score": 0.995, "text": "have access to Jinja's built-in functions,such as manipulating strings and structured data. For each" }, { "category_id": 15, "poly": [ 295.0, 1369.0, 1404.0, 1369.0, 1404.0, 1403.0, 295.0, 1403.0 ], "score": 0.989, "text": " template, prompts are created by applying the template to all examples in the corresponding dataset." }, { "category_id": 15, "poly": [ 295.0, 344.0, 1405.0, 344.0, 1405.0, 376.0, 295.0, 376.0 ], "score": 0.99, "text": "In order to collect hundreds of templates for prompts, we first needed a system that enabled users to" }, { "category_id": 15, "poly": [ 297.0, 374.0, 1403.0, 374.0, 1403.0, 406.0, 297.0, 406.0 ], "score": 0.987, "text": "view data, provide templates in a standard format,and verify that their templates work correctly. We" }, { "category_id": 15, "poly": [ 297.0, 405.0, 1405.0, 405.0, 1405.0, 438.0, 297.0, 438.0 ], "score": 0.992, "text": "implemented a lightweight interface in Streamlit that users could download, run locally in a web" }, { "category_id": 15, "poly": [ 294.0, 433.0, 974.0, 433.0, 974.0, 470.0, 294.0, 470.0 ], "score": 0.988, "text": "browser, and then upload their results to a central repository." }, { "category_id": 15, "poly": [ 294.0, 226.0, 940.0, 226.0, 940.0, 268.0, 294.0, 268.0 ], "score": 0.987, "text": "DANNOTATION SYSTEM - PROMPTSOURCE" } ], "page_info": { "page_no": 20, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 915, 1403, 915, 1403, 1129, 298, 1129 ], "score": 0.982 }, { "category_id": 1, "poly": [ 298, 655, 1403, 655, 1403, 900, 298, 900 ], "score": 0.98 }, { "category_id": 1, "poly": [ 298, 394, 1403, 394, 1403, 639, 298, 639 ], "score": 0.98 }, { "category_id": 1, "poly": [ 298, 1619, 1403, 1619, 1403, 1805, 298, 1805 ], "score": 0.979 }, { "category_id": 1, "poly": [ 298, 1819, 1402, 1819, 1402, 2034, 298, 2034 ], "score": 0.979 }, { "category_id": 1, "poly": [ 299, 1145, 1402, 1145, 1402, 1298, 299, 1298 ], "score": 0.977 }, { "category_id": 1, "poly": [ 298, 1451, 1402, 1451, 1402, 1604, 298, 1604 ], "score": 0.975 }, { "category_id": 0, "poly": [ 298, 226, 502, 226, 502, 261, 298, 261 ], "score": 0.882 }, { "category_id": 2, "poly": [ 299, 75, 815, 75, 815, 104, 299, 104 ], "score": 0.881 }, { "category_id": 2, "poly": [ 835, 2088, 864, 2088, 864, 2112, 835, 2112 ], "score": 0.845 }, { "category_id": 0, "poly": [ 299, 321, 830, 321, 830, 352, 299, 352 ], "score": 0.777 }, { "category_id": 0, "poly": [ 298, 1377, 885, 1377, 885, 1408, 298, 1408 ], "score": 0.705 }, { "category_id": 1, "poly": [ 298, 1377, 885, 1377, 885, 1408, 298, 1408 ], "score": 0.201 }, { "category_id": 1, "poly": [ 299, 321, 830, 321, 830, 352, 299, 352 ], "score": 0.115 }, { "category_id": 15, "poly": [ 292.0, 222.0, 507.0, 222.0, 507.0, 269.0, 292.0, 269.0 ], "score": 0.999, "text": "EDATASETS" }, { "category_id": 15, "poly": [ 296.0, 72.0, 817.0, 72.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.989, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2084.0, 871.0, 2084.0, 871.0, 2125.0, 829.0, 2125.0 ], "score": 1.0, "text": "22" }, { "category_id": 15, "poly": [ 294.0, 319.0, 835.0, 319.0, 835.0, 356.0, 294.0, 356.0 ], "score": 0.99, "text": "E.1CATEGORIZING DATASETS INTO TASKS" }, { "category_id": 15, "poly": [ 294.0, 1376.0, 888.0, 1376.0, 888.0, 1412.0, 294.0, 1412.0 ], "score": 0.996, "text": "E.2HOW UNSEEN ARE THE HELD-OUT TASKS?" }, { "category_id": 15, "poly": [ 291.0, 913.0, 1405.0, 913.0, 1405.0, 952.0, 291.0, 952.0 ], "score": 0.976, "text": "For comparison, Brown et al. (2020, p.17) define a commonsense task as an “attempt to capture" }, { "category_id": 15, "poly": [ 294.0, 947.0, 1405.0, 947.0, 1405.0, 982.0, 294.0, 982.0 ], "score": 0.99, "text": "physical or scientific reasoning,as distinct from sentence completion, reading comprehension, or" }, { "category_id": 15, "poly": [ 294.0, 977.0, 1403.0, 977.0, 1403.0, 1011.0, 294.0, 1011.0 ], "score": 0.985, "text": "broad knowledge question answering.”Circular definition aside, it is far from clear that scientific" }, { "category_id": 15, "poly": [ 292.0, 1007.0, 1405.0, 1007.0, 1405.0, 1040.0, 292.0, 1040.0 ], "score": 0.982, "text": "reasoning is commonsense. Among Brown et al. (2O2O)'s selection, ARC exemplifies how evaluation" }, { "category_id": 15, "poly": [ 294.0, 1038.0, 1404.0, 1038.0, 1404.0, 1072.0, 294.0, 1072.0 ], "score": 0.998, "text": "of scientific knowledge goes far beyond commonsense.Despite being constructed from grade school" }, { "category_id": 15, "poly": [ 292.0, 1067.0, 1405.0, 1067.0, 1405.0, 1103.0, 292.0, 1103.0 ], "score": 0.981, "text": " science questions,authors of this paper find most of ARC difficult to answer(and,to a lesser degree," }, { "category_id": 15, "poly": [ 296.0, 1099.0, 522.0, 1099.0, 522.0, 1135.0, 296.0, 1135.0 ], "score": 0.964, "text": "OpenBookQA too)." }, { "category_id": 15, "poly": [ 294.0, 657.0, 1404.0, 657.0, 1404.0, 690.0, 294.0, 690.0 ], "score": 0.99, "text": "In principle,categorizing by content makes more sense than by format. Most humans would con-" }, { "category_id": 15, "poly": [ 294.0, 686.0, 1405.0, 686.0, 1405.0, 720.0, 294.0, 720.0 ], "score": 0.977, "text": " sider taking an exam in history vs.in physics as two different tasks,whereas whether the exam" }, { "category_id": 15, "poly": [ 292.0, 716.0, 1403.0, 716.0, 1403.0, 749.0, 292.0, 749.0 ], "score": 0.987, "text": " is multiple-choice or extractive matters less.By this logic, it is relatively uncontroversial to estab-" }, { "category_id": 15, "poly": [ 294.0, 746.0, 1405.0, 746.0, 1405.0, 780.0, 294.0, 780.0 ], "score": 0.988, "text": "lish closed-book QA as a distinct task, which largely evaluates a model's memorization of world" }, { "category_id": 15, "poly": [ 293.0, 776.0, 1403.0, 776.0, 1403.0, 812.0, 293.0, 812.0 ], "score": 0.981, "text": "knowledge (Roberts et al., 2O2O). The distinction between commonsense and (mere) reading com-" }, { "category_id": 15, "poly": [ 294.0, 809.0, 1405.0, 809.0, 1405.0, 842.0, 294.0, 842.0 ], "score": 0.994, "text": "prehension, however,is much more blurry. As mentioned in Section 3,there are vast differences in" }, { "category_id": 15, "poly": [ 294.0, 840.0, 1405.0, 840.0, 1405.0, 873.0, 294.0, 873.0 ], "score": 0.971, "text": "what is considered as commonsense by each dataset's authors. To oversimplify, they usually include" }, { "category_id": 15, "poly": [ 293.0, 869.0, 1132.0, 869.0, 1132.0, 904.0, 293.0, 904.0 ], "score": 0.988, "text": "questions that evaluate physical cognition and (US-centric) cultural norms." }, { "category_id": 15, "poly": [ 296.0, 396.0, 1404.0, 396.0, 1404.0, 429.0, 296.0, 429.0 ], "score": 0.991, "text": "Our task taxonomy (Figure 2) consists of mostly straightforward decisions that reflect well-known" }, { "category_id": 15, "poly": [ 294.0, 427.0, 1403.0, 427.0, 1403.0, 460.0, 294.0, 460.0 ], "score": 0.992, "text": "tasks in the literature: sentiment analysis,topic classification, paraphrase identification, natural lan-" }, { "category_id": 15, "poly": [ 292.0, 455.0, 1404.0, 455.0, 1404.0, 494.0, 292.0, 494.0 ], "score": 0.989, "text": " guage inference, word sense disambiguation, coreference resolution, summarization, and structure-" }, { "category_id": 15, "poly": [ 294.0, 487.0, 1403.0, 487.0, 1403.0, 520.0, 294.0, 520.0 ], "score": 0.984, "text": "to-text generation.The main diffculty lies in the fact that a large collection of datasets are all com-" }, { "category_id": 15, "poly": [ 292.0, 517.0, 1405.0, 517.0, 1405.0, 552.0, 292.0, 552.0 ], "score": 0.984, "text": " monly known as“question answering”,and there is no commonly accepted way of subdividing this" }, { "category_id": 15, "poly": [ 292.0, 546.0, 1405.0, 546.0, 1405.0, 582.0, 292.0, 582.0 ], "score": 0.995, "text": "category. CrossFit and UnifiedQA categorize them by format (multiple-choice vs. extractive vs. ab-" }, { "category_id": 15, "poly": [ 292.0, 576.0, 1407.0, 576.0, 1407.0, 615.0, 292.0, 615.0 ], "score": 0.975, "text": " stractive/generative), whereas Brown et al. (2O2O) categorize by content (reading comprehension vs." }, { "category_id": 15, "poly": [ 293.0, 608.0, 702.0, 608.0, 702.0, 644.0, 293.0, 644.0 ], "score": 0.972, "text": "commonsense Vs. closed-book QA)." }, { "category_id": 15, "poly": [ 293.0, 1619.0, 1405.0, 1619.0, 1405.0, 1653.0, 293.0, 1653.0 ], "score": 0.991, "text": "One common concern is that paraphrasing identification is too similar to NLI and should also be held" }, { "category_id": 15, "poly": [ 292.0, 1648.0, 1405.0, 1648.0, 1405.0, 1688.0, 292.0, 1688.0 ], "score": 0.993, "text": "out. We disagree for two reasons.First, NLI tests for unidirectional entailment, while paraphrasing" }, { "category_id": 15, "poly": [ 292.0, 1679.0, 1407.0, 1679.0, 1407.0, 1717.0, 292.0, 1717.0 ], "score": 0.983, "text": " asks for bidirectional entailment. An author manually reviewed ANLI and RTE and found almost no" }, { "category_id": 15, "poly": [ 294.0, 1713.0, 1404.0, 1713.0, 1404.0, 1745.0, 294.0, 1745.0 ], "score": 0.985, "text": "entailment examples that are also valid paraphrases. Second, it has been shown (e.g., Pruksachatkun" }, { "category_id": 15, "poly": [ 293.0, 1740.0, 1404.0, 1740.0, 1404.0, 1776.0, 293.0, 1776.0 ], "score": 0.986, "text": "et al.,2O2O) that training on a paraphrase dataset (QQP) before training on an NLI dataset (RTE)" }, { "category_id": 15, "poly": [ 294.0, 1773.0, 1148.0, 1773.0, 1148.0, 1808.0, 294.0, 1808.0 ], "score": 0.992, "text": " actually hurts performance compared to training on the entailment task only." }, { "category_id": 15, "poly": [ 296.0, 1821.0, 1404.0, 1821.0, 1404.0, 1855.0, 296.0, 1855.0 ], "score": 0.989, "text": "Another tricky category that has been challnged as too similar to NLI is sentence completion:" }, { "category_id": 15, "poly": [ 294.0, 1850.0, 1401.0, 1850.0, 1401.0, 1885.0, 294.0, 1885.0 ], "score": 0.992, "text": "choosing the most plausible option which continues or completes a sentence or a short paragraph." }, { "category_id": 15, "poly": [ 293.0, 1880.0, 1406.0, 1880.0, 1406.0, 1917.0, 293.0, 1917.0 ], "score": 0.985, "text": "SWAG was proposed as “commonsense inference” to supplement NLI, but the distinction between" }, { "category_id": 15, "poly": [ 292.0, 1910.0, 1406.0, 1910.0, 1406.0, 1946.0, 292.0, 1946.0 ], "score": 0.983, "text": "formal semanticists' deductive inference and natural pragmatic inference is not clearly drawn in most" }, { "category_id": 15, "poly": [ 292.0, 1940.0, 1404.0, 1940.0, 1404.0, 1977.0, 292.0, 1977.0 ], "score": 0.987, "text": "NLI datasets (Pavlick and Kwiatkowski, 2O19).Additionally, coreference and any“continuation-" }, { "category_id": 15, "poly": [ 293.0, 1973.0, 1406.0, 1973.0, 1406.0, 2006.0, 293.0, 2006.0 ], "score": 0.981, "text": " style\" prompt could also be interpreted as a sentence completion task.These blurry boundaries have" }, { "category_id": 15, "poly": [ 294.0, 2005.0, 1150.0, 2005.0, 1150.0, 2036.0, 294.0, 2036.0 ], "score": 0.993, "text": "no clear answers.So we categorically hold out the sentence completion task." }, { "category_id": 15, "poly": [ 295.0, 1144.0, 1404.0, 1144.0, 1404.0, 1181.0, 295.0, 1181.0 ], "score": 0.991, "text": "Finally, note that NLI and coreference datasets (especially the newer ones such as ANLI and Wino-" }, { "category_id": 15, "poly": [ 294.0, 1175.0, 1406.0, 1175.0, 1406.0, 1210.0, 294.0, 1210.0 ], "score": 0.983, "text": " grande) all in practice require commonsense knowledge. Therefore,we find it difcult to establish" }, { "category_id": 15, "poly": [ 292.0, 1206.0, 1406.0, 1206.0, 1406.0, 1240.0, 292.0, 1240.0 ], "score": 0.989, "text": "commonsense as a standalone category of task, defaulting back to categorizing QAs by their for-" }, { "category_id": 15, "poly": [ 295.0, 1237.0, 1404.0, 1237.0, 1404.0, 1270.0, 295.0, 1270.0 ], "score": 0.985, "text": "mat. This implies that we categorize ARC as multiple-choice QA, because other closed-book QAs" }, { "category_id": 15, "poly": [ 293.0, 1266.0, 1055.0, 1266.0, 1055.0, 1303.0, 293.0, 1303.0 ], "score": 0.999, "text": "require generating the answer without any provided answer options." }, { "category_id": 15, "poly": [ 296.0, 1454.0, 1406.0, 1454.0, 1406.0, 1487.0, 296.0, 1487.0 ], "score": 0.988, "text": "Because “question answering” is so broadly defined, QA datasets could have included entailment" }, { "category_id": 15, "poly": [ 294.0, 1480.0, 1404.0, 1480.0, 1404.0, 1517.0, 294.0, 1517.0 ], "score": 0.993, "text": "or coreference questions,rendering them not strictly held-out tasks.For example, ReCoRD is an" }, { "category_id": 15, "poly": [ 294.0, 1514.0, 1404.0, 1514.0, 1404.0, 1547.0, 294.0, 1547.0 ], "score": 0.994, "text": "extractive QA dataset that exclusively asks questions which amount to identifying a referent. We" }, { "category_id": 15, "poly": [ 293.0, 1543.0, 1406.0, 1543.0, 1406.0, 1580.0, 293.0, 1580.0 ], "score": 0.99, "text": "hold out ReCoRD as part of SuperGLUE, but it is impractical to inspect every dataset and slice out" }, { "category_id": 15, "poly": [ 293.0, 1572.0, 1093.0, 1572.0, 1093.0, 1611.0, 293.0, 1611.0 ], "score": 0.99, "text": "the subsets of examples which ask entailment or coreference questions." }, { "category_id": 15, "poly": [ 294.0, 1376.0, 888.0, 1376.0, 888.0, 1412.0, 294.0, 1412.0 ], "score": 0.996, "text": "E.2HOW UNSEEN ARE THE HELD-OUT TASKS?" }, { "category_id": 15, "poly": [ 294.0, 319.0, 835.0, 319.0, 835.0, 356.0, 294.0, 356.0 ], "score": 0.996, "text": "E.1CATEGORIZING DATASETS INTO TASKS" } ], "page_info": { "page_no": 21, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 298, 417, 1404, 417, 1404, 814, 298, 814 ], "score": 0.983 }, { "category_id": 1, "poly": [ 298, 829, 1404, 829, 1404, 1135, 298, 1135 ], "score": 0.981 }, { "category_id": 1, "poly": [ 300, 229, 1401, 229, 1401, 322, 300, 322 ], "score": 0.957 }, { "category_id": 0, "poly": [ 299, 359, 516, 359, 516, 391, 299, 391 ], "score": 0.892 }, { "category_id": 2, "poly": [ 333, 2006, 770, 2006, 770, 2034, 333, 2034 ], "score": 0.89 }, { "category_id": 2, "poly": [ 300, 76, 815, 76, 815, 104, 300, 104 ], "score": 0.884 }, { "category_id": 1, "poly": [ 299, 1230, 436, 1230, 436, 1261, 299, 1261 ], "score": 0.872 }, { "category_id": 0, "poly": [ 300, 1173, 675, 1173, 675, 1203, 300, 1203 ], "score": 0.872 }, { "category_id": 2, "poly": [ 835, 2088, 864, 2088, 864, 2112, 835, 2112 ], "score": 0.844 }, { "category_id": 13, "poly": [ 992, 983, 1068, 983, 1068, 1012, 992, 1012 ], "score": 0.89, "latex": "40 . 5 \\%" }, { "category_id": 13, "poly": [ 438, 892, 515, 892, 515, 921, 438, 921 ], "score": 0.88, "latex": "7 2 . 5 \\%" }, { "category_id": 13, "poly": [ 943, 753, 997, 753, 997, 782, 943, 782 ], "score": 0.87, "latex": "57 \\%" }, { "category_id": 13, "poly": [ 972, 861, 1033, 861, 1033, 890, 972, 890 ], "score": 0.87, "latex": "6 . 2 \\%" }, { "category_id": 13, "poly": [ 1320, 983, 1394, 983, 1394, 1012, 1320, 1012 ], "score": 0.86, "latex": "1 0 . 7 \\%" }, { "category_id": 13, "poly": [ 755, 953, 830, 953, 830, 982, 755, 982 ], "score": 0.86, "latex": "2 7 . 8 \\%" }, { "category_id": 13, "poly": [ 540, 922, 614, 922, 614, 951, 540, 951 ], "score": 0.86, "latex": "1 8 . 7 \\%" }, { "category_id": 13, "poly": [ 454, 831, 549, 831, 549, 860, 454, 860 ], "score": 0.82, "latex": "_ { \\mathrm { T 5 + L M } }" }, { "category_id": 13, "poly": [ 1120, 983, 1214, 983, 1214, 1012, 1120, 1012 ], "score": 0.78, "latex": "_ { \\mathrm { T 5 + L M } }" }, { "category_id": 15, "poly": [ 294.0, 357.0, 520.0, 357.0, 520.0, 397.0, 294.0, 397.0 ], "score": 1.0, "text": "E.3LAMBADA" }, { "category_id": 15, "poly": [ 332.0, 2001.0, 773.0, 2001.0, 773.0, 2039.0, 332.0, 2039.0 ], "score": 0.996, "text": "8https://github.com/openai/gpt-2/issues/131" }, { "category_id": 15, "poly": [ 296.0, 73.0, 817.0, 73.0, 817.0, 108.0, 296.0, 108.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 295.0, 1172.0, 677.0, 1172.0, 677.0, 1207.0, 295.0, 1207.0 ], "score": 0.974, "text": "E.4TABLE OF ALL DATASETS" }, { "category_id": 15, "poly": [ 828.0, 2084.0, 871.0, 2084.0, 871.0, 2123.0, 828.0, 2123.0 ], "score": 1.0, "text": "23" }, { "category_id": 15, "poly": [ 295.0, 415.0, 1404.0, 415.0, 1404.0, 452.0, 295.0, 452.0 ], "score": 0.988, "text": "As described above, our task categorization is overall somewhat similar to that of Brown et al." }, { "category_id": 15, "poly": [ 296.0, 450.0, 1405.0, 450.0, 1405.0, 482.0, 296.0, 482.0 ], "score": 0.977, "text": "(2020). One additional exception is the LAMBADA dataset (Paperno et al.,2016),which Brown" }, { "category_id": 15, "poly": [ 292.0, 478.0, 1404.0, 478.0, 1404.0, 513.0, 292.0, 513.0 ], "score": 0.975, "text": "et al. (202O) clasify as part of the“sentence completion” task group.LAMBADA differs signifi-" }, { "category_id": 15, "poly": [ 292.0, 507.0, 1404.0, 507.0, 1404.0, 546.0, 292.0, 546.0 ], "score": 0.989, "text": "cantly from the other tasks in this group since it requires open-ended next word prediction (rather" }, { "category_id": 15, "poly": [ 293.0, 539.0, 1404.0, 539.0, 1404.0, 575.0, 293.0, 575.0 ], "score": 0.984, "text": " than choosing among a few possible continuations). The dataset was designed in this way specifi-" }, { "category_id": 15, "poly": [ 292.0, 565.0, 1405.0, 565.0, 1405.0, 610.0, 292.0, 610.0 ], "score": 0.985, "text": " cally so that its format is exactly the same as standard language modeling, thereby alowing language" }, { "category_id": 15, "poly": [ 293.0, 599.0, 1405.0, 599.0, 1405.0, 635.0, 293.0, 635.0 ], "score": 0.988, "text": "models to be evaluated on it without additional fine-tuning or adaptation. Brown et al. (2O2O) deviate" }, { "category_id": 15, "poly": [ 294.0, 631.0, 1406.0, 631.0, 1406.0, 667.0, 294.0, 667.0 ], "score": 0.988, "text": "from standard practice on this benchmark in the following ways: First, they introduce a prompted" }, { "category_id": 15, "poly": [ 293.0, 661.0, 1408.0, 661.0, 1408.0, 697.0, 293.0, 697.0 ], "score": 0.976, "text": "form that converts it to a fill-in-the-blank-style task. Second, they evaluate on a non-standard format" }, { "category_id": 15, "poly": [ 292.0, 691.0, 1405.0, 691.0, 1405.0, 725.0, 292.0, 725.0 ], "score": 0.986, "text": "of the dataset that omits the tokenization and lowercasing of the oficial benchmark.8 Third, GPT-3" }, { "category_id": 15, "poly": [ 296.0, 723.0, 1404.0, 723.0, 1404.0, 755.0, 296.0, 755.0 ], "score": 0.984, "text": "was trained on the Book Corpus dataset, which is the same dataset that was used as a source of all" }, { "category_id": 15, "poly": [ 292.0, 751.0, 942.0, 751.0, 942.0, 789.0, 292.0, 789.0 ], "score": 0.968, "text": " passages in LAMBADA. Brown et al. (2020) estimate that" }, { "category_id": 15, "poly": [ 998.0, 751.0, 1405.0, 751.0, 1405.0, 789.0, 998.0, 789.0 ], "score": 0.977, "text": "of the LAMBADA test set examples" }, { "category_id": 15, "poly": [ 293.0, 784.0, 668.0, 784.0, 668.0, 820.0, 293.0, 820.0 ], "score": 0.996, "text": "appeared in GPT-3's training set." }, { "category_id": 15, "poly": [ 296.0, 830.0, 453.0, 830.0, 453.0, 865.0, 296.0, 865.0 ], "score": 0.995, "text": "We evaluated" }, { "category_id": 15, "poly": [ 550.0, 830.0, 1404.0, 830.0, 1404.0, 865.0, 550.0, 865.0 ], "score": 0.996, "text": "on the standard LAMBADA dataset in the original unprompted next-word-" }, { "category_id": 15, "poly": [ 293.0, 860.0, 971.0, 860.0, 971.0, 894.0, 293.0, 894.0 ], "score": 0.986, "text": " prediction form and found that it achieved an accuracy of" }, { "category_id": 15, "poly": [ 1034.0, 860.0, 1405.0, 860.0, 1405.0, 894.0, 1034.0, 894.0 ], "score": 0.981, "text": ". This is substantially below the" }, { "category_id": 15, "poly": [ 294.0, 891.0, 437.0, 891.0, 437.0, 926.0, 294.0, 926.0 ], "score": 0.99, "text": "accuracy of" }, { "category_id": 15, "poly": [ 516.0, 891.0, 1406.0, 891.0, 1406.0, 926.0, 516.0, 926.0 ], "score": 0.993, "text": "achieved by the comparably-sized GPT-3-13B variant. TO did not fare much" }, { "category_id": 15, "poly": [ 294.0, 921.0, 539.0, 921.0, 539.0, 957.0, 294.0, 957.0 ], "score": 0.98, "text": "better, achieving only" }, { "category_id": 15, "poly": [ 615.0, 921.0, 1406.0, 921.0, 1406.0, 957.0, 615.0, 957.0 ], "score": 0.985, "text": ". We therefore evaluated using the same cloze-style prompted form used" }, { "category_id": 15, "poly": [ 294.0, 952.0, 754.0, 952.0, 754.0, 987.0, 294.0, 987.0 ], "score": 0.965, "text": "by GPT-3, which raised TO's accuracy to" }, { "category_id": 15, "poly": [ 831.0, 952.0, 1406.0, 952.0, 1406.0, 987.0, 831.0, 987.0 ], "score": 0.973, "text": ". If we swap out the official LAMBADA dataset for" }, { "category_id": 15, "poly": [ 296.0, 983.0, 991.0, 983.0, 991.0, 1015.0, 296.0, 1015.0 ], "score": 0.969, "text": "the variant used by GPT-3, TO's accuracy further increases to" }, { "category_id": 15, "poly": [ 1069.0, 983.0, 1119.0, 983.0, 1119.0, 1015.0, 1069.0, 1015.0 ], "score": 1.0, "text": "and" }, { "category_id": 15, "poly": [ 1215.0, 983.0, 1319.0, 983.0, 1319.0, 1015.0, 1215.0, 1015.0 ], "score": 1.0, "text": "achieves" }, { "category_id": 15, "poly": [ 1395.0, 983.0, 1405.0, 983.0, 1405.0, 1015.0, 1395.0, 1015.0 ], "score": 0.638, "text": "：" }, { "category_id": 15, "poly": [ 296.0, 1014.0, 1402.0, 1014.0, 1402.0, 1045.0, 296.0, 1045.0 ], "score": 0.994, "text": "We suspect that the additional gap between TO and GPT-3-13B's performance is at least partially" }, { "category_id": 15, "poly": [ 294.0, 1043.0, 1405.0, 1043.0, 1405.0, 1078.0, 294.0, 1078.0 ], "score": 0.992, "text": "due to the fact that GPT-3 was trained on a large portion of LAMBADA's test set. Due to this" }, { "category_id": 15, "poly": [ 293.0, 1072.0, 1406.0, 1072.0, 1406.0, 1109.0, 293.0, 1109.0 ], "score": 0.994, "text": "discrepancy and the fact that LAMBADA is dissimilar to the other sentence completion tasks, we" }, { "category_id": 15, "poly": [ 296.0, 1105.0, 764.0, 1105.0, 764.0, 1137.0, 296.0, 1137.0 ], "score": 0.984, "text": "omitted LAMBADA from our evaluation." }, { "category_id": 15, "poly": [ 291.0, 226.0, 1406.0, 226.0, 1406.0, 267.0, 291.0, 267.0 ], "score": 0.99, "text": " Evaluation datasets in BIG-bench were created with the goal of testing language models on diverse," }, { "category_id": 15, "poly": [ 295.0, 260.0, 1402.0, 260.0, 1402.0, 294.0, 295.0, 294.0 ], "score": 0.987, "text": "difficult,and novel skills.Therefore,those datasets are unlikely to have high overlap with T0's" }, { "category_id": 15, "poly": [ 294.0, 290.0, 461.0, 290.0, 461.0, 329.0, 294.0, 329.0 ], "score": 0.997, "text": "training tasks." }, { "category_id": 15, "poly": [ 294.0, 1228.0, 442.0, 1228.0, 442.0, 1264.0, 294.0, 1264.0 ], "score": 0.992, "text": "See Table 5." } ], "page_info": { "page_no": 22, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 308, 207, 1389, 207, 1389, 1964, 308, 1964 ], "score": 0.959, "html": "
TaskDatasetTO TrainTO+ TrainTO++ TrainEval
Coreference Resolutionsuper_glue/wsc.fixed√√
Coreference Resolutionwinogrande/winogrande_xl√
Natural Language Inferencesuper_glue/cb√
Natural Language Inferencesuper_glue/rte anli√
Natural Language Inference√
Paraphrase Identification glue/mrpc
Paraphrase Identification glue/qqp
Paraphrase Identificationpaws/labeled_final>>>>>>>>>~
Closed-Book QAai2_arc/ARC_Challenge
Closed-Book QAai2_arc/ARC_Easy
Closed-Book QAkilt_tasks/hotpotqa√
Closed-Book QAtrivia_qa/unfiltered√
Closed-Book QAweb-questions√
Closed-Book QAwiki-qa√
Extractive QAadversarial_qa/dbidaf
Extractive QAadversarial_qa/dbert
Extractive QAadversarial_qa/droberta
Extractive QAduorc/SelfRC
Extractive QAduorc/ParaphraseRC<>>>>>>
Extractive QAropes>>>>>>
Extractive QAsquad_v2√
Extractive QAsuper_glue/record
Extractive QAquoref√√
Extractive QAtydiqa√
Multiple-Choice QAcos_e/v1.11√
Multiple-Choice QAcosmos_qa√√
Multiple-Choice QAdream√√
Multiple-Choice QAopenbookqa/main√
Multiple-Choice QAqasc√
Multiple-Choice QAquail√
Multiple-Choice QAquarel√√
Multiple-Choice QAquartz√√
Multiple-Choice QArace/high√
Multiple-Choice QArace/middle√
Multiple-Choice QAsciq√√
Multiple-Choice QAsocial_i_qa√√
Multiple-Choice QAsuper_glue/boolq
Multiple-Choice QAsuper_glue/multirc
Multiple-Choice QAwiki_hop/original√>>>>>>
Multiple-Choice QAwiqa√
Multiple-Choice QApiqa
Sentimentamazon_polarity√
Sentimentapp-reviews√
Sentimentimdb√
Sentimentrotten_tomatoes√√
Sentimentyelp_review_full√√
Sentence Completionsuper-glue/copa
Sentence Completionstory_cloze/2016<>>
Sentence Completionhellaswag
Structure-to-Textcommon_gen
Structure-to-Textwiki_bio
Summarizationcnn_dailymail/3.0.0
Summarizationgigaword
Summarizationmulti_news
Summarizationsamsum
SummarizationXsum>>>>>>>>>
Topic Classificationag_news>>>>>>>>>>>
Topic Classificationdbpedia_14
Topic Classificationtrec√√
Word Sense Disambiguationsuper-glue/wic
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TaskCBHellaSwagLambadaStory ClozeWiCWinograndeWSC
Matches1/250912/1000015/51533/187120/14000/17674/146
TaskANLI premisesANLI hypothesesRTE premisesRTE hypotheses
Matches337/10006/1000329/3000156/3000
" }, { "category_id": 2, "poly": [ 299, 75, 816, 75, 816, 105, 299, 105 ], "score": 0.893 }, { "category_id": 1, "poly": [ 298, 664, 1404, 664, 1404, 758, 298, 758 ], "score": 0.861 }, { "category_id": 2, "poly": [ 835, 2088, 865, 2088, 865, 2112, 835, 2112 ], "score": 0.844 }, { "category_id": 0, "poly": [ 290, 226, 1367, 226, 1367, 262, 290, 262 ], "score": 0.835 }, { "category_id": 6, "poly": [ 298, 664, 1404, 664, 1404, 758, 298, 758 ], "score": 0.102 }, { "category_id": 13, "poly": [ 297, 1072, 359, 1072, 359, 1102, 297, 1102 ], "score": 0.86, "latex": "5 . 2 \\%" }, { "category_id": 13, "poly": [ 608, 1133, 684, 1133, 684, 1162, 608, 1162 ], "score": 0.86, "latex": "9 . 1 2 \\%" }, { "category_id": 15, "poly": [ 295.0, 73.0, 817.0, 73.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.99, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2084.0, 870.0, 2084.0, 870.0, 2124.0, 829.0, 2124.0 ], "score": 1.0, "text": "25" }, { "category_id": 15, "poly": [ 292.0, 223.0, 1369.0, 223.0, 1369.0, 268.0, 292.0, 268.0 ], "score": 0.997, "text": "FCONTAMINATION ANALYSIS OF PRETRAINING CORPUS ON TEST TASKS" }, { "category_id": 15, "poly": [ 294.0, 665.0, 1404.0, 665.0, 1404.0, 699.0, 294.0, 699.0 ], "score": 0.986, "text": "For NLI datasets,we separate matches for premises and hypotheses since,the premises tend to be" }, { "category_id": 15, "poly": [ 293.0, 695.0, 1404.0, 695.0, 1404.0, 729.0, 293.0, 729.0 ], "score": 0.989, "text": "sourced from the internet and therefore have a high number of matches.However, if the hypothesis" }, { "category_id": 15, "poly": [ 293.0, 726.0, 975.0, 726.0, 975.0, 760.0, 293.0, 760.0 ], "score": 0.982, "text": "it is paired with is novel, memorization might not be helpful." }, { "category_id": 15, "poly": [ 294.0, 466.0, 1405.0, 466.0, 1405.0, 498.0, 294.0, 498.0 ], "score": 0.987, "text": "In order to do this effectively,we use the sufix array method described and implemented in Lee" }, { "category_id": 15, "poly": [ 292.0, 494.0, 1406.0, 494.0, 1406.0, 531.0, 292.0, 531.0 ], "score": 0.986, "text": "et al. (2021) to index C4,allowing us to run fast counts of how many times a substring appears in" }, { "category_id": 15, "poly": [ 294.0, 525.0, 1406.0, 525.0, 1406.0, 560.0, 294.0, 560.0 ], "score": 0.989, "text": "the corpus.To limit the number of queries,we search by partitioning sentences into groups of 16" }, { "category_id": 15, "poly": [ 294.0, 556.0, 1405.0, 556.0, 1405.0, 592.0, 294.0, 592.0 ], "score": 0.992, "text": "tokens and doing an exact match query. This gives us an over-counting on how many length-32 token" }, { "category_id": 15, "poly": [ 296.0, 588.0, 1404.0, 588.0, 1404.0, 620.0, 296.0, 620.0 ], "score": 0.992, "text": "overlaps there are in the corpus. We flag examples that produce a match during that procedure, then" }, { "category_id": 15, "poly": [ 293.0, 616.0, 562.0, 616.0, 562.0, 652.0, 293.0, 652.0 ], "score": 0.996, "text": "manually inspect them." }, { "category_id": 15, "poly": [ 296.0, 297.0, 1404.0, 297.0, 1404.0, 330.0, 296.0, 330.0 ], "score": 0.994, "text": "Zero-shot performance estimation can be confounded if the pretraining corpus for the model contains" }, { "category_id": 15, "poly": [ 295.0, 327.0, 1404.0, 327.0, 1404.0, 361.0, 295.0, 361.0 ], "score": 0.995, "text": "text from the test tasks because models could improve performance through memorization rather" }, { "category_id": 15, "poly": [ 292.0, 355.0, 1405.0, 355.0, 1405.0, 395.0, 292.0, 395.0 ], "score": 0.99, "text": " than generalization. In order to control for this effect, we searched for long common substrings" }, { "category_id": 15, "poly": [ 295.0, 388.0, 1404.0, 388.0, 1404.0, 422.0, 295.0, 422.0 ], "score": 0.997, "text": "between the input examples (presented in prompted form) for our zero-shot test tasks on one hand," }, { "category_id": 15, "poly": [ 296.0, 419.0, 1066.0, 419.0, 1066.0, 452.0, 296.0, 452.0 ], "score": 0.982, "text": "and documents in C4 (our model's pretraining set) on the other hand." }, { "category_id": 15, "poly": [ 297.0, 980.0, 1403.0, 980.0, 1403.0, 1013.0, 297.0, 1013.0 ], "score": 0.968, "text": "As expected, ANLI and RTE return a high proportion of matches on the premises. However, ANLI" }, { "category_id": 15, "poly": [ 294.0, 1009.0, 1403.0, 1009.0, 1403.0, 1047.0, 294.0, 1047.0 ], "score": 0.99, "text": "hypotheses have negligible overlap with the pretraining set, which prevents pretraining memoriza-" }, { "category_id": 15, "poly": [ 294.0, 1040.0, 1405.0, 1040.0, 1405.0, 1077.0, 294.0, 1077.0 ], "score": 0.987, "text": "tion from solving the task. On the contrary, RTE hypotheses are contained in the pretraining dataset" }, { "category_id": 15, "poly": [ 360.0, 1070.0, 1405.0, 1070.0, 1405.0, 1107.0, 360.0, 1107.0 ], "score": 0.984, "text": "of time.Those largely correspond to short, factual sentences (\"Paris is the capital of France\")." }, { "category_id": 15, "poly": [ 294.0, 1100.0, 1406.0, 1100.0, 1406.0, 1141.0, 294.0, 1141.0 ], "score": 0.978, "text": " Those are examples where the pretraining dataset could help if factual knowledge helps with soly-" }, { "category_id": 15, "poly": [ 294.0, 1132.0, 607.0, 1132.0, 607.0, 1167.0, 294.0, 1167.0 ], "score": 0.982, "text": "ing the task. HellaSwag has" }, { "category_id": 15, "poly": [ 685.0, 1132.0, 1405.0, 1132.0, 1405.0, 1167.0, 685.0, 1167.0 ], "score": 0.983, "text": "matches,which could be problematic as it is a continuation task:" }, { "category_id": 15, "poly": [ 294.0, 1161.0, 1406.0, 1161.0, 1406.0, 1200.0, 294.0, 1200.0 ], "score": 0.991, "text": " the correct answer is also contained in the same original internet page as the input sequence, even" }, { "category_id": 15, "poly": [ 295.0, 1193.0, 1406.0, 1193.0, 1406.0, 1230.0, 295.0, 1230.0 ], "score": 0.993, "text": "though the multiple-choice answering format prevents the model from just generating the correct" }, { "category_id": 15, "poly": [ 296.0, 1225.0, 1205.0, 1225.0, 1205.0, 1258.0, 296.0, 1258.0 ], "score": 0.989, "text": "answer verbatim through memorization. Other datasets are free of contamination." }, { "category_id": 15, "poly": [ 294.0, 665.0, 1404.0, 665.0, 1404.0, 699.0, 294.0, 699.0 ], "score": 0.986, "text": "For NLI datasets,we separate matches for premises and hypotheses since,the premises tend to be" }, { "category_id": 15, "poly": [ 293.0, 695.0, 1404.0, 695.0, 1404.0, 729.0, 293.0, 729.0 ], "score": 0.989, "text": "sourced from the internet and therefore have a high number of matches.However, if the hypothesis" }, { "category_id": 15, "poly": [ 293.0, 726.0, 975.0, 726.0, 975.0, 760.0, 293.0, 760.0 ], "score": 0.982, "text": "it is paired with is novel, memorization might not be helpful." } ], "page_info": { "page_no": 24, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 206, 1211, 1506, 1211, 1506, 1578, 206, 1578 ], "score": 0.976, "html": "
T5+LMT0 (p =1)T0 (p = 5.7)T0 (3B)TOTO+TO++
TaskDatasetMeanMed.MeanMed.MeanMed.MeanMed.MeanMed.MeanMed.MeanMed.
Coref.wsC54.0957.6952.4056.2560.0063.4665.1064.4261.4564.4262.2464.4270.2969.71
Wino. (XL)50.6550.7158.1157.2259.3558.8050.9750.5159.9460.4662.5461.7266.4266.54
NLIANLI R1 ANLIR232.89 33.7632.85 32.9039.02 36.9640.05 38.2041.28 37.7943.20 38.6033.84 33.1133.65 33.4043.5644.70 39.4043.45 39.7745.80 41.1047.07 42.1849.80 44.50
ANLIR333.8233.7538.0939.3338.3338.5833.3333.3338.68 41.2642.4240.7641.1744.0946.42
CB34.3433.9348.8550.8954.4064.2945.3650.0070.1278.5759.2071.4375.6983.93
RTE53.0351.8176.4379.2475.6774.9164.5564.0880.8381.2367.4764.9885.3184.84
Compl.COPA54.8855.0087.6687.5090.8591.6972.4074.9290.0290.7992.2493.8893.7193.75
HellaSwag27.0027.7332.7933.2735.2035.2027.2927.5133.5833.6586.1385.7986.1185.65
StoryCloze48.1648.8589.5793.0095.4595.8884.0385.0992.4094.7196.4397.1796.4997.33
WSDWiC50.3050.2455.0354.9455.0054.9450.6950.3956.5857.2155.0255.4970.0269.98
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Greyed-out text corresponds to" }, { "category_id": 15, "poly": [ 296.0, 1630.0, 623.0, 1630.0, 623.0, 1662.0, 296.0, 1662.0 ], "score": 0.979, "text": "results that are not zero-shot." }, { "category_id": 15, "poly": [ 294.0, 219.0, 577.0, 219.0, 577.0, 270.0, 294.0, 270.0 ], "score": 0.994, "text": "GFULL RESULTS" } ], "page_info": { "page_no": 25, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 525, 849, 1176, 849, 1176, 1341, 525, 1341 ], "score": 0.978, "html": "
DatasetT5-LMTOTO+TO++
Code Description18.3336.6753.3358.33
Conceptual25.0062.5081.2575.00
Hindu Knowledge32.0036.0038.2940.00
Known Unknowns52.1763.0463.0452.17
Language ID16.7120.6820.8022.17
Logic Grid31.0039.6039.5039.40
Logical Deduction31.0055.4044.2043.60
Misconceptions51.6052.5152.9754.79
Movie Dialog50.1953.8354.0553.97
Novel Concepts9.3815.6231.2528.12
Strategy QA52.2552.7354.0054.39
Syllogisms50.0451.7950.5350.31
Vitamin C38.2964.7366.2470.00
Winowhy45.7747.3845.8448.15
" }, { "category_id": 2, "poly": [ 299, 75, 815, 75, 815, 105, 299, 105 ], "score": 0.883 }, { "category_id": 2, "poly": [ 835, 2088, 864, 2088, 864, 2112, 835, 2112 ], "score": 0.848 }, { "category_id": 7, "poly": [ 456, 1364, 1243, 1364, 1243, 1396, 456, 1396 ], "score": 0.424 }, { "category_id": 15, "poly": [ 295.0, 73.0, 817.0, 73.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.99, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2084.0, 870.0, 2084.0, 870.0, 2125.0, 829.0, 2125.0 ], "score": 1.0, "text": "27" }, { "category_id": 15, "poly": [ 453.0, 1361.0, 1245.0, 1361.0, 1245.0, 1399.0, 453.0, 1399.0 ], "score": 0.994, "text": "Table 7: Results for TO model variants on a subset of BIG-bench tasks." } ], "page_info": { "page_no": 26, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 299, 296, 1405, 296, 1405, 389, 299, 389 ], "score": 0.968 }, { "category_id": 0, "poly": [ 301, 225, 685, 225, 685, 262, 301, 262 ], "score": 0.893 }, { "category_id": 2, "poly": [ 299, 75, 816, 75, 816, 105, 299, 105 ], "score": 0.868 }, { "category_id": 2, "poly": [ 835, 2088, 865, 2088, 865, 2112, 835, 2112 ], "score": 0.839 }, { "category_id": 15, "poly": [ 295.0, 225.0, 688.0, 225.0, 688.0, 266.0, 295.0, 266.0 ], "score": 0.967, "text": "H LIST OF ALL PROMPTS" }, { "category_id": 15, "poly": [ 295.0, 73.0, 817.0, 73.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.989, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2084.0, 871.0, 2084.0, 871.0, 2123.0, 829.0, 2123.0 ], "score": 1.0, "text": "28" }, { "category_id": 15, "poly": [ 294.0, 294.0, 1405.0, 294.0, 1405.0, 332.0, 294.0, 332.0 ], "score": 0.997, "text": "The following appendix enumerates all prompts used to train and evaluate the various TO models" }, { "category_id": 15, "poly": [ 294.0, 326.0, 1402.0, 326.0, 1402.0, 359.0, 294.0, 359.0 ], "score": 0.98, "text": " as reported in this paper. 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300.0 ], "score": 0.948, "text": "{{answer_choices[1]]]" }, { "category_id": 15, "poly": [ 295.0, 1628.0, 575.0, 1628.0, 575.0, 1668.0, 295.0, 1668.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 508.0, 573.0, 508.0, 573.0, 546.0, 295.0, 546.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 1082.0, 505.0, 1082.0, 505.0, 1121.0, 304.0, 1121.0 ], "score": 0.999, "text": "{{sent_less}}" }, { "category_id": 15, "poly": [ 302.0, 1107.0, 478.0, 1107.0, 478.0, 1150.0, 302.0, 1150.0 ], "score": 0.978, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 298.0, 1305.0, 550.0, 1305.0, 550.0, 1342.0, 298.0, 1342.0 ], "score": 0.998, "text": "A:{{sent_more}}" }, { "category_id": 15, "poly": [ 300.0, 1334.0, 549.0, 1334.0, 549.0, 1368.0, 300.0, 1368.0 ], "score": 0.998, "text": "B:{{sent_less}}" }, { "category_id": 15, "poly": [ 301.0, 1362.0, 1376.0, 1362.0, 1376.0, 1396.0, 301.0, 1396.0 ], "score": 0.993, "text": "I believe sentence A is conveying a stereotype. Is that correct? Answer" }, { "category_id": 15, "poly": [ 300.0, 1388.0, 537.0, 1388.0, 537.0, 1427.0, 300.0, 1427.0 ], "score": 0.991, "text": "with yes or no." }, { "category_id": 15, "poly": [ 305.0, 812.0, 660.0, 812.0, 660.0, 843.0, 305.0, 843.0 ], "score": 0.989, "text": "{% if stereo_antistereo" }, { "category_id": 16, "poly": [ 729.0, 812.0, 734.0, 812.0, 734.0, 843.0, 729.0, 843.0 ], "score": 0.531, "text": "（2" }, { "category_id": 15, "poly": [ 736.0, 814.0, 771.0, 814.0, 771.0, 842.0, 736.0, 842.0 ], "score": 0.99, "text": "%}" }, { "category_id": 15, "poly": [ 300.0, 836.0, 539.0, 836.0, 539.0, 871.0, 300.0, 871.0 ], "score": 0.992, "text": "\"{{sent_more}}\"" }, { "category_id": 15, "poly": [ 299.0, 862.0, 1346.0, 862.0, 1346.0, 903.0, 299.0, 903.0 ], "score": 0.997, "text": " The previous sentence illustrates a stereotype against a historically" }, { "category_id": 15, "poly": [ 301.0, 895.0, 1390.0, 895.0, 1390.0, 927.0, 301.0, 927.0 ], "score": 0.985, "text": "disadvantaged group in the US. Minimally modify the previous sentence to" }, { "category_id": 15, "poly": [ 296.0, 922.0, 1374.0, 922.0, 1374.0, 958.0, 296.0, 958.0 ], "score": 0.994, "text": "a less stereotypical sentence involving a contrasting advantaged group." }, { "category_id": 15, "poly": [ 301.0, 566.0, 1241.0, 566.0, 1241.0, 599.0, 301.0, 599.0 ], "score": 0.982, "text": " Sentence A demonstrates a stereotype Ill Sentence B violates a" }, { "category_id": 15, "poly": [ 304.0, 597.0, 461.0, 597.0, 461.0, 625.0, 304.0, 625.0 ], "score": 1.0, "text": "stereotype" }, { "category_id": 15, "poly": [ 294.0, 722.0, 933.0, 722.0, 933.0, 760.0, 294.0, 760.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 758.0, 462.0, 758.0, 462.0, 790.0, 296.0, 790.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1815.0, 933.0, 1815.0, 933.0, 1853.0, 294.0, 1853.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1851.0, 462.0, 1851.0, 462.0, 1883.0, 296.0, 1883.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1218.0, 933.0, 1218.0, 933.0, 1256.0, 294.0, 1256.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1254.0, 462.0, 1254.0, 462.0, 1286.0, 296.0, 1286.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 308.0, 435.0, 863.0, 435.0, 863.0, 466.0, 308.0, 466.0 ], "score": 0.989, "text": "{{answer_choices[stereo_antistereo]}}" }, { "category_id": 15, "poly": [ 300.0, 1904.0, 1059.0, 1904.0, 1059.0, 1938.0, 300.0, 1938.0 ], "score": 0.999, "text": "You are presented with two very similar sentences:" }, { "category_id": 15, "poly": [ 298.0, 1930.0, 550.0, 1930.0, 550.0, 1966.0, 298.0, 1966.0 ], "score": 0.998, "text": "A:{{sent_more}}" }, { "category_id": 15, "poly": [ 298.0, 1959.0, 552.0, 1959.0, 552.0, 1994.0, 298.0, 1994.0 ], "score": 0.999, "text": "B:{{sent_less}}" }, { "category_id": 15, "poly": [ 299.0, 1988.0, 1166.0, 1988.0, 1166.0, 2021.0, 299.0, 2021.0 ], "score": 0.982, "text": " Sentence B is illustrating an anti-stereotype. 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"score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 296.0, 448.0, 467.0, 448.0, 467.0, 489.0, 296.0, 489.0 ], "score": 0.951, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 298.0, 1544.0, 464.0, 1544.0, 464.0, 1583.0, 298.0, 1583.0 ], "score": 0.903, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 294.0, 1982.0, 475.0, 1982.0, 475.0, 2021.0, 294.0, 2021.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 306.0, 944.0, 504.0, 944.0, 504.0, 977.0, 306.0, 977.0 ], "score": 0.999, "text": "{{sent_more}}" }, { "category_id": 15, "poly": [ 307.0, 971.0, 475.0, 971.0, 475.0, 1006.0, 307.0, 1006.0 ], "score": 0.967, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 300.0, 1163.0, 685.0, 1163.0, 685.0, 1200.0, 300.0, 1200.0 ], "score": 0.978, "text": "Sentence 1: {{sent_more}}" }, { "category_id": 15, "poly": [ 300.0, 1192.0, 686.0, 1192.0, 686.0, 1227.0, 300.0, 1227.0 ], "score": 0.98, "text": "Sentence 2: {{sent_less}}" }, { "category_id": 15, "poly": [ 300.0, 1220.0, 1332.0, 1220.0, 1332.0, 1254.0, 300.0, 1254.0 ], "score": 0.988, "text": "I think that Sentence 2 conveys an anti-stereotype. Is that correct?" }, { "category_id": 15, "poly": [ 300.0, 1250.0, 567.0, 1250.0, 567.0, 1285.0, 300.0, 1285.0 ], "score": 0.999, "text": "Answer yes or no." }, { "category_id": 15, "poly": [ 303.0, 1765.0, 1358.0, 1765.0, 1358.0, 1796.0, 303.0, 1796.0 ], "score": 0.998, "text": "Here are two sentences. One is more stereotypical than the other. What" }, { "category_id": 15, "poly": [ 300.0, 1790.0, 1392.0, 1790.0, 1392.0, 1827.0, 300.0, 1827.0 ], "score": 0.983, "text": "kind of stereotypes are these two sentences\" illustrating? Choose between" }, { "category_id": 15, "poly": [ 303.0, 1816.0, 907.0, 1816.0, 907.0, 1857.0, 303.0, 1857.0 ], "score": 0.929, "text": "{{answer_choices| join(\"，\"） i lower}}." }, { "category_id": 15, "poly": [ 302.0, 1847.0, 682.0, 1847.0, 682.0, 1883.0, 302.0, 1883.0 ], "score": 0.988, "text": "Sentence 1: {{sent_more}}" }, { "category_id": 15, "poly": [ 302.0, 1874.0, 682.0, 1874.0, 682.0, 1909.0, 302.0, 1909.0 ], "score": 0.986, "text": "Sentence 2: {{sent_less}}" }, { "category_id": 15, "poly": [ 295.0, 582.0, 932.0, 582.0, 932.0, 616.0, 295.0, 616.0 ], "score": 0.995, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 614.0, 462.0, 614.0, 462.0, 646.0, 296.0, 646.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 669.0, 660.0, 669.0, 660.0, 701.0, 305.0, 701.0 ], "score": 0.979, "text": "{% if stereo_antistereo" }, { "category_id": 16, "poly": [ 770.0, 672.0, 773.0, 672.0, 773.0, 700.0, 770.0, 700.0 ], "score": 0.482, "text": "2号" }, { "category_id": 15, "poly": [ 300.0, 691.0, 541.0, 691.0, 541.0, 730.0, 300.0, 730.0 ], "score": 0.981, "text": "\"{{sent_less}}\"" }, { "category_id": 15, "poly": [ 301.0, 722.0, 1300.0, 722.0, 1300.0, 759.0, 301.0, 759.0 ], "score": 1.0, "text": "The previous sentence violates a stereotype against a historically" }, { "category_id": 15, "poly": [ 301.0, 751.0, 1393.0, 751.0, 1393.0, 787.0, 301.0, 787.0 ], "score": 0.99, "text": "disadvantaged group in the US. Minimally modify the previous sentence to" }, { "category_id": 15, "poly": [ 299.0, 778.0, 1358.0, 778.0, 1358.0, 816.0, 299.0, 816.0 ], "score": 0.997, "text": "a less stereotypical sentence involving a contrasting advantage group." }, { "category_id": 15, "poly": [ 294.0, 1677.0, 933.0, 1677.0, 933.0, 1714.0, 294.0, 1714.0 ], "score": 0.98, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 1710.0, 462.0, 1710.0, 462.0, 1742.0, 295.0, 1742.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1078.0, 933.0, 1078.0, 933.0, 1114.0, 294.0, 1114.0 ], "score": 0.997, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1112.0, 462.0, 1112.0, 462.0, 1144.0, 296.0, 1144.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 945.0, 505.0, 945.0, 505.0, 976.0, 306.0, 976.0 ], "score": 0.997, "text": "{{sent_more}}" }, { "category_id": 15, "poly": [ 303.0, 972.0, 477.0, 972.0, 477.0, 1006.0, 303.0, 1006.0 ], "score": 0.983, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 294.0, 1487.0, 575.0, 1487.0, 575.0, 1527.0, 294.0, 1527.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 307.0, 320.0, 863.0, 320.0, 863.0, 351.0, 307.0, 351.0 ], "score": 0.991, "text": "{{answer_choices[stereo_antistereo]}}" }, { "category_id": 15, "poly": [ 308.0, 1416.0, 863.0, 1416.0, 863.0, 1447.0, 308.0, 1447.0 ], "score": 0.989, "text": "{{answer_choices[stereo_antistereo]}}" }, { "category_id": 15, "poly": [ 294.0, 1982.0, 475.0, 1982.0, 475.0, 2021.0, 294.0, 2021.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 391.0, 575.0, 391.0, 575.0, 431.0, 295.0, 431.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 293.0, 260.0, 474.0, 260.0, 474.0, 298.0, 293.0, 298.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1356.0, 474.0, 1356.0, 474.0, 1394.0, 293.0, 1394.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 886.0, 474.0, 886.0, 474.0, 924.0, 293.0, 924.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 298.0, 1544.0, 464.0, 1544.0, 464.0, 1583.0, 298.0, 1583.0 ], "score": 0.903, "text": "No IIl Yes" } ], "page_info": { "page_no": 32, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 298, 819, 1298, 819, 1298, 993, 298, 993 ], "score": 0.957, "html": "
KeyValue
premiseThe taxpayer met with the accountant to get help f...
hypothesisThe accountant sought help filing taxes.
idx0
label1
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(2O18). Used in evaluation." }, { "category_id": 15, "poly": [ 294.0, 1130.0, 713.0, 1130.0, 713.0, 1164.0, 294.0, 1164.0 ], "score": 0.992, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 1163.0, 463.0, 1163.0, 463.0, 1195.0, 295.0, 1195.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 300.0, 381.0, 1348.0, 381.0, 1348.0, 417.0, 300.0, 417.0 ], "score": 0.984, "text": "Race and color Ill Socioeconomic status and occupation Ill Gender and" }, { "category_id": 15, "poly": [ 301.0, 412.0, 1348.0, 412.0, 1348.0, 446.0, 301.0, 446.0 ], "score": 0.98, "text": "gender identity Ill Disability Ill Nationality IIl Sexual orientation" }, { "category_id": 15, "poly": [ 302.0, 439.0, 973.0, 439.0, 973.0, 476.0, 302.0, 476.0 ], "score": 0.977, "text": "Ill Physical appearance IIl Religion Ill Age" }, { "category_id": 15, "poly": [ 300.0, 1220.0, 1391.0, 1220.0, 1391.0, 1255.0, 300.0, 1255.0 ], "score": 0.986, "text": " Given {{premise}} Should we assume that \"{{hypothesis}}\" is true? 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0.89, "text": "Yes III No" }, { "category_id": 15, "poly": [ 295.0, 805.0, 574.0, 805.0, 574.0, 840.0, 295.0, 840.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 300.0, 864.0, 465.0, 864.0, 465.0, 902.0, 300.0, 902.0 ], "score": 0.89, "text": "Yes III No" }, { "category_id": 15, "poly": [ 301.0, 289.0, 465.0, 289.0, 465.0, 327.0, 301.0, 327.0 ], "score": 0.897, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 301.0, 1988.0, 523.0, 1988.0, 523.0, 2020.0, 301.0, 2020.0 ], "score": 0.966, "text": "True IIl False" }, { "category_id": 15, "poly": [ 301.0, 1988.0, 523.0, 1988.0, 523.0, 2020.0, 301.0, 2020.0 ], "score": 0.966, "text": "True IIl False" }, { "category_id": 15, "poly": [ 297.0, 863.0, 467.0, 863.0, 467.0, 904.0, 297.0, 904.0 ], "score": 0.938, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 306.0, 519.0, 1180.0, 519.0, 1180.0, 552.0, 306.0, 552.0 ], "score": 0.969, "text": "{{premise}} Based on the previous passage， is it 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Yes or" }, { "category_id": 15, "poly": [ 300.0, 406.0, 355.0, 406.0, 355.0, 432.0, 300.0, 432.0 ], "score": 0.917, "text": "no?" }, { "category_id": 15, "poly": [ 306.0, 1620.0, 714.0, 1620.0, 714.0, 1657.0, 306.0, 1657.0 ], "score": 0.981, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 306.0, 573.0, 714.0, 573.0, 714.0, 610.0, 306.0, 610.0 ], "score": 0.98, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 304.0, 1445.0, 1395.0, 1445.0, 1395.0, 1485.0, 304.0, 1485.0 ], "score": 0.993, "text": "{{premise}} Are we justified in saying that \"{{hypothesis}}\"? Yes or no?" }, { "category_id": 15, "poly": [ 304.0, 1079.0, 714.0, 1079.0, 714.0, 1118.0, 304.0, 1118.0 ], "score": 0.969, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 295.0, 1896.0, 712.0, 1896.0, 712.0, 1930.0, 295.0, 1930.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 1930.0, 462.0, 1930.0, 462.0, 1962.0, 295.0, 1962.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 300.0, 909.0, 1331.0, 909.0, 1331.0, 945.0, 300.0, 945.0 ], "score": 0.991, "text": "Given that {{premise}} Does it follow that {{hypothesis}} Yes or no?" }, { "category_id": 15, "poly": [ 295.0, 1358.0, 713.0, 1358.0, 713.0, 1392.0, 295.0, 1392.0 ], "score": 0.989, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 1390.0, 462.0, 1390.0, 462.0, 1423.0, 295.0, 1423.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 298.0, 1987.0, 1305.0, 1987.0, 1305.0, 2022.0, 298.0, 2022.0 ], "score": 0.981, "text": " Suppose {{premise}} Can we infer that \"{{hypothesis}}\"? 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textMark told Pete many lies about himself，which Pete...
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(2012). Used in evaluation." }, { "category_id": 15, "poly": [ 305.0, 690.0, 1393.0, 690.0, 1393.0, 725.0, 305.0, 725.0 ], "score": 0.983, "text": "{{premise}} Using only the above description and what you know about the" }, { "category_id": 15, "poly": [ 299.0, 715.0, 1166.0, 715.0, 1166.0, 752.0, 299.0, 752.0 ], "score": 0.977, "text": "world, is \"{{hypothesis}}\" definitely correct? Yes or no?" }, { "category_id": 15, "poly": [ 295.0, 1865.0, 706.0, 1865.0, 706.0, 1904.0, 295.0, 1904.0 ], "score": 0.986, "text": "Prompt from Schick and Schuitze (2021)" }, { "category_id": 15, "poly": [ 295.0, 1900.0, 461.0, 1900.0, 461.0, 1933.0, 295.0, 1933.0 ], "score": 0.997, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 1959.0, 1159.0, 1959.0, 1159.0, 1993.0, 305.0, 1993.0 ], "score": 0.971, "text": "{{ text }} In the previous sentence，does the pronoun \"{{" }, { "category_id": 15, "poly": [ 300.0, 1980.0, 1214.0, 1980.0, 1214.0, 2026.0, 300.0, 2026.0 ], "score": 0.951, "text": "span2_text.lower() }}\" refer to {{ spanl_text }]? 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0.933, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 295.0, 402.0, 574.0, 402.0, 574.0, 437.0, 295.0, 437.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 939.0, 574.0, 939.0, 574.0, 974.0, 295.0, 974.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 298.0, 460.0, 464.0, 460.0, 464.0, 498.0, 298.0, 498.0 ], "score": 0.891, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 298.0, 460.0, 464.0, 460.0, 464.0, 498.0, 298.0, 498.0 ], "score": 0.892, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 305.0, 1195.0, 460.0, 1195.0, 460.0, 1230.0, 305.0, 1230.0 ], "score": 0.935, "text": "{{ text }}" }, { "category_id": 15, "poly": [ 294.0, 1528.0, 575.0, 1528.0, 575.0, 1568.0, 294.0, 1568.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 298.0, 1239.0, 1220.0, 1239.0, 1220.0, 1293.0, 298.0, 1293.0 ], "score": 0.931, "text": "In other words, {{ text.split(\" \")[span2_index:]_I join(\" \")" }, { "category_id": 15, "poly": [ 300.0, 1275.0, 1048.0, 1275.0, 1048.0, 1312.0, 300.0, 1312.0 ], "score": 0.975, "text": "replace(span2_text， spanl_text) }} True or false?" }, { "category_id": 15, "poly": [ 307.0, 1784.0, 1374.0, 1784.0, 1374.0, 1823.0, 307.0, 1823.0 ], "score": 0.985, "text": "{{ text }} I think they mean \"{{ text.split(\" \")[span2_index:] I join(\"" }, { "category_id": 15, "poly": [ 303.0, 1812.0, 1077.0, 1812.0, 1077.0, 1850.0, 303.0, 1850.0 ], "score": 0.959, "text": "\")| replace(span2_text， spanl_text) }}\" Yes or no?" }, { "category_id": 15, "poly": [ 303.0, 655.0, 1393.0, 655.0, 1393.0, 694.0, 303.0, 694.0 ], "score": 0.98, "text": "{{ text }} Here, by \"{{ span2_text }}\" they mean \"{{ spanl_text }}\". Yes" }, { "category_id": 15, "poly": [ 302.0, 687.0, 403.0, 687.0, 403.0, 719.0, 302.0, 719.0 ], "score": 0.996, "text": "or no?" }, { "category_id": 15, "poly": [ 304.0, 856.0, 499.0, 856.0, 499.0, 894.0, 304.0, 894.0 ], "score": 0.985, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 856.0, 1179.0, 856.0, 1179.0, 894.0, 524.0, 894.0 ], "score": 0.967, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 320.0, 498.0, 320.0, 498.0, 358.0, 305.0, 358.0 ], "score": 0.972, "text": "{% if label !" }, { "category_id": 15, "poly": [ 523.0, 320.0, 1179.0, 320.0, 1179.0, 358.0, 523.0, 358.0 ], "score": 0.968, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 1447.0, 498.0, 1447.0, 498.0, 1487.0, 306.0, 1487.0 ], "score": 0.978, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1447.0, 1178.0, 1447.0, 1178.0, 1487.0, 524.0, 1487.0 ], "score": 0.968, "text": " -1 %}{{ 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"category_id": 13, "poly": [ 500, 689, 523, 689, 523, 710, 500, 710 ], "score": 0.31, "latex": "=" }, { "category_id": 13, "poly": [ 488, 686, 564, 686, 564, 713, 488, 713 ], "score": 0.26, "latex": "! = ~ - 1" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 294.0, 1830.0, 475.0, 1830.0, 475.0, 1869.0, 294.0, 1869.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 870.0, 2085.0, 870.0, 2123.0, 830.0, 2123.0 ], "score": 1.0, "text": "39" }, { "category_id": 15, "poly": [ 295.0, 1633.0, 463.0, 1633.0, 463.0, 1669.0, 295.0, 1669.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 228.0, 574.0, 228.0, 574.0, 266.0, 295.0, 266.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 424.0, 464.0, 424.0, 464.0, 460.0, 295.0, 460.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 622.0, 475.0, 622.0, 475.0, 660.0, 294.0, 660.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1297.0, 474.0, 1297.0, 474.0, 1335.0, 293.0, 1335.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1438.0, 574.0, 1438.0, 574.0, 1473.0, 295.0, 1473.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 763.0, 574.0, 763.0, 574.0, 798.0, 295.0, 798.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 298.0, 820.0, 464.0, 820.0, 464.0, 859.0, 298.0, 859.0 ], "score": 0.903, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 299.0, 287.0, 464.0, 287.0, 464.0, 325.0, 299.0, 325.0 ], "score": 0.908, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 299.0, 1494.0, 464.0, 1494.0, 464.0, 1533.0, 299.0, 1533.0 ], "score": 0.937, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 304.0, 1690.0, 1377.0, 1690.0, 1377.0, 1727.0, 304.0, 1727.0 ], "score": 0.978, "text": "{{ text }} In the previous sentence, can the pronoun \"{{ span2_text }}\"" }, { "category_id": 15, "poly": [ 298.0, 1717.0, 1016.0, 1717.0, 1016.0, 1755.0, 298.0, 1755.0 ], "score": 0.973, "text": " be replaced with \"{{ spanl_text }}\"? Yes or no?" }, { "category_id": 15, "poly": [ 305.0, 481.0, 1392.0, 481.0, 1392.0, 519.0, 305.0, 519.0 ], "score": 0.968, "text": "{{ text }} Here,does \"{{ span2_text.lower() }}\" stand for {{ spanl_text" }, { "category_id": 15, "poly": [ 305.0, 511.0, 523.0, 511.0, 523.0, 543.0, 305.0, 543.0 ], "score": 0.955, "text": "}}? Yes or no?" }, { "category_id": 15, "poly": [ 300.0, 1100.0, 1316.0, 1100.0, 1316.0, 1137.0, 300.0, 1137.0 ], "score": 0.977, "text": " Question: In the passage above, does the pronoun \"{{ span2_text }}\"" }, { "category_id": 15, "poly": [ 301.0, 1130.0, 702.0, 1130.0, 702.0, 1164.0, 301.0, 1164.0 ], "score": 0.957, "text": " refer to {{ spanl_text }}?" }, { "category_id": 15, "poly": [ 305.0, 681.0, 487.0, 681.0, 487.0, 719.0, 305.0, 719.0 ], "score": 0.959, "text": "{% if label " }, { "category_id": 15, "poly": [ 565.0, 681.0, 1179.0, 681.0, 1179.0, 719.0, 565.0, 719.0 ], "score": 0.975, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 1889.0, 498.0, 1889.0, 498.0, 1927.0, 305.0, 1927.0 ], "score": 0.973, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1889.0, 1178.0, 1889.0, 1178.0, 1927.0, 524.0, 1927.0 ], "score": 0.969, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 1968.0, 574.0, 1968.0, 574.0, 2008.0, 295.0, 2008.0 ], "score": 0.99, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 301.0, 1185.0, 417.0, 1185.0, 417.0, 1220.0, 301.0, 1220.0 ], "score": 1.0, "text": "Answer:" }, { "category_id": 15, "poly": [ 304.0, 1355.0, 498.0, 1355.0, 498.0, 1394.0, 304.0, 1394.0 ], "score": 0.988, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1355.0, 1177.0, 1355.0, 1177.0, 1394.0, 524.0, 1394.0 ], "score": 0.96, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 301.0, 1046.0, 595.0, 1046.0, 595.0, 1083.0, 301.0, 1083.0 ], "score": 0.979, "text": "Passage: {{ text }}" }, { "category_id": 15, "poly": [ 295.0, 1438.0, 574.0, 1438.0, 574.0, 1473.0, 295.0, 1473.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 763.0, 574.0, 763.0, 574.0, 798.0, 295.0, 798.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 954.0, 631.0, 954.0, 631.0, 993.0, 295.0, 993.0 ], "score": 0.983, "text": "Prompt from Brown et al. (2020)" }, { "category_id": 15, "poly": [ 296.0, 991.0, 463.0, 991.0, 463.0, 1023.0, 296.0, 1023.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1297.0, 474.0, 1297.0, 474.0, 1335.0, 293.0, 1335.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 622.0, 475.0, 622.0, 475.0, 660.0, 294.0, 660.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 424.0, 464.0, 424.0, 464.0, 460.0, 295.0, 460.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 228.0, 574.0, 228.0, 574.0, 266.0, 295.0, 266.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1633.0, 463.0, 1633.0, 463.0, 1669.0, 295.0, 1669.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 299.0, 287.0, 464.0, 287.0, 464.0, 325.0, 299.0, 325.0 ], "score": 0.908, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 299.0, 1494.0, 464.0, 1494.0, 464.0, 1533.0, 299.0, 1533.0 ], "score": 0.937, "text": "No IIl Yes" } ], "page_info": { "page_no": 38, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 304, 1792, 1392, 1792, 1392, 2026, 304, 2026 ], "score": 0.952 }, { "category_id": 1, "poly": [ 306, 498, 1356, 498, 1356, 638, 306, 638 ], "score": 0.926 }, { "category_id": 0, "poly": [ 298, 1734, 458, 1734, 458, 1763, 298, 1763 ], "score": 0.885 }, { "category_id": 1, "poly": [ 306, 841, 1174, 841, 1174, 872, 306, 872 ], "score": 0.86 }, { "category_id": 1, "poly": [ 305, 1449, 1174, 1449, 1174, 1480, 305, 1480 ], "score": 0.855 }, { "category_id": 2, "poly": [ 298, 74, 816, 74, 816, 106, 298, 106 ], "score": 0.848 }, { "category_id": 1, "poly": [ 296, 1124, 705, 1124, 705, 1185, 296, 1185 ], "score": 0.844 }, { "category_id": 1, "poly": [ 305, 667, 412, 667, 412, 694, 305, 694 ], "score": 0.84 }, { "category_id": 1, "poly": [ 299, 1533, 570, 1533, 570, 1563, 299, 1563 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834, 2113 ], "score": 0.219 }, { "category_id": 0, "poly": [ 303, 1597, 519, 1597, 519, 1626, 303, 1626 ], "score": 0.145 }, { "category_id": 1, "poly": [ 303, 244, 459, 244, 459, 275, 303, 275 ], "score": 0.13 }, { "category_id": 2, "poly": [ 287, 237, 1400, 237, 1400, 284, 287, 284 ], "score": 0.113 }, { "category_id": 1, "poly": [ 297, 381, 459, 381, 459, 412, 297, 412 ], "score": 0.113 }, { "category_id": 1, "poly": [ 298, 1387, 468, 1387, 468, 1417, 298, 1417 ], "score": 0.098 }, { "category_id": 0, "poly": [ 305, 443, 591, 443, 591, 472, 305, 472 ], "score": 0.096 }, { "category_id": 0, "poly": [ 299, 1533, 570, 1533, 570, 1563, 299, 1563 ], "score": 0.091 }, { "category_id": 13, "poly": [ 1168, 1825, 1211, 1825, 1211, 1849, 1168, 1849 ], "score": 0.8, "latex": "= =" }, { "category_id": 13, "poly": [ 1169, 501, 1211, 501, 1211, 525, 1169, 525 ], "score": 0.79, "latex": "= =" }, { "category_id": 13, "poly": [ 691, 1826, 733, 1826, 733, 1848, 691, 1848 ], "score": 0.77, "latex": "= =" }, { "category_id": 13, "poly": [ 691, 502, 732, 502, 732, 523, 691, 523 ], "score": 0.75, "latex": "= =" }, { "category_id": 13, "poly": [ 500, 845, 523, 845, 523, 866, 500, 866 ], "score": 0.41, "latex": "=" }, { "category_id": 13, "poly": [ 499, 1453, 523, 1453, 523, 1475, 499, 1475 ], "score": 0.35, "latex": "=" }, { "category_id": 15, "poly": [ 295.0, 1731.0, 463.0, 1731.0, 463.0, 1768.0, 295.0, 1768.0 ], "score": 0.972, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 1384.0, 474.0, 1384.0, 474.0, 1422.0, 293.0, 1422.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 379.0, 464.0, 379.0, 464.0, 415.0, 295.0, 415.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 829.0, 2085.0, 871.0, 2085.0, 871.0, 2122.0, 829.0, 2122.0 ], "score": 1.0, "text": "40" }, { "category_id": 15, "poly": [ 293.0, 776.0, 474.0, 776.0, 474.0, 814.0, 293.0, 814.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 920.0, 575.0, 920.0, 575.0, 960.0, 294.0, 960.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 829.0, 2085.0, 871.0, 2085.0, 871.0, 2121.0, 829.0, 2121.0 ], "score": 1.0, "text": "40" }, { "category_id": 15, "poly": [ 300.0, 1593.0, 524.0, 1593.0, 524.0, 1628.0, 300.0, 1628.0 ], "score": 0.974, "text": "False IIl True" }, { "category_id": 15, "poly": [ 295.0, 236.0, 468.0, 236.0, 468.0, 281.0, 295.0, 281.0 ], "score": 0.941, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 301.0, 438.0, 594.0, 438.0, 594.0, 476.0, 301.0, 476.0 ], "score": 0.94, "text": "Context: {{ text }}" }, { "category_id": 15, "poly": [ 294.0, 1528.0, 575.0, 1528.0, 575.0, 1568.0, 294.0, 1568.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 1793.0, 456.0, 1793.0, 456.0, 1827.0, 304.0, 1827.0 ], "score": 0.972, "text": "{{ text }}" }, { "category_id": 15, "poly": [ 301.0, 1816.0, 690.0, 1816.0, 690.0, 1857.0, 301.0, 1857.0 ], "score": 0.987, "text": "{% if span2_text.lower()" }, { "category_id": 15, "poly": [ 734.0, 1816.0, 1167.0, 1816.0, 1167.0, 1857.0, 734.0, 1857.0 ], "score": 0.968, "text": " \"they\" or span2_text.lower()" }, { "category_id": 15, "poly": [ 1212.0, 1816.0, 1363.0, 1816.0, 1363.0, 1857.0, 1212.0, 1857.0 ], "score": 0.977, "text": "\"them\" %}" }, { "category_id": 15, "poly": [ 301.0, 1848.0, 1375.0, 1848.0, 1375.0, 1884.0, 301.0, 1884.0 ], "score": 0.981, "text": "Question: Who or what are \"{{ span2_text.lower() }}\"? {{ spanl_text }}?" }, { "category_id": 15, "poly": [ 303.0, 1872.0, 459.0, 1872.0, 459.0, 1909.0, 303.0, 1909.0 ], "score": 0.978, "text": "{% else %}" }, { "category_id": 15, "poly": [ 301.0, 1901.0, 1389.0, 1901.0, 1389.0, 1938.0, 301.0, 1938.0 ], "score": 0.991, "text": "Question: Who or what is \"{{ span2_text.lower() }}\"? Is it {{ spanl_text" }, { "category_id": 15, "poly": [ 301.0, 1930.0, 360.0, 1930.0, 360.0, 1966.0, 301.0, 1966.0 ], "score": 0.921, "text": "}}？" }, { "category_id": 15, "poly": [ 303.0, 1957.0, 476.0, 1957.0, 476.0, 1994.0, 303.0, 1994.0 ], "score": 0.976, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 299.0, 1984.0, 418.0, 1984.0, 418.0, 2021.0, 299.0, 2021.0 ], "score": 1.0, "text": "Answer:" }, { "category_id": 15, "poly": [ 303.0, 495.0, 668.0, 495.0, 668.0, 529.0, 303.0, 529.0 ], "score": 0.997, "text": "{% if span2_text.lower()" }, { "category_id": 15, "poly": [ 734.0, 496.0, 1163.0, 496.0, 1163.0, 532.0, 734.0, 532.0 ], "score": 0.992, "text": " \"they\" or span2_text.lower()" }, { "category_id": 15, "poly": [ 1214.0, 498.0, 1314.0, 498.0, 1314.0, 527.0, 1214.0, 527.0 ], "score": 0.999, "text": "\"them\"" }, { "category_id": 15, "poly": [ 1320.0, 496.0, 1358.0, 496.0, 1358.0, 529.0, 1320.0, 529.0 ], "score": 0.871, "text": "%}" }, { "category_id": 15, "poly": [ 303.0, 525.0, 448.0, 525.0, 448.0, 558.0, 303.0, 558.0 ], "score": 1.0, "text": "Question:" }, { "category_id": 15, "poly": [ 451.0, 520.0, 1286.0, 520.0, 1286.0, 560.0, 451.0, 560.0 ], "score": 0.968, "text": "\"{{ span2_text }}\" are {{ spanl_text }}. 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KeyValue
answer2
option1Ian
option2Dennis
sentenceIan .volunteered to eat Dennis's menudo
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No IIl
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KeyValue
hypothesis the language was peeled down
idx label0 0
premiseIt was a complex language. Not written down but ha...
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Template:" }, { "category_id": 15, "poly": [ 293.0, 591.0, 474.0, 591.0, 474.0, 629.0, 293.0, 629.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1636.0, 474.0, 1636.0, 474.0, 1674.0, 293.0, 1674.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1258.0, 575.0, 1258.0, 575.0, 1298.0, 294.0, 1298.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1766.0, 575.0, 1766.0, 575.0, 1806.0, 294.0, 1806.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1954.0, 712.0, 1954.0, 712.0, 1988.0, 295.0, 1988.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 294.0, 1985.0, 463.0, 1985.0, 463.0, 2020.0, 294.0, 2020.0 ], "score": 0.996, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 721.0, 575.0, 721.0, 575.0, 761.0, 294.0, 761.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1447.0, 463.0, 1447.0, 463.0, 1483.0, 295.0, 1483.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 296.0, 911.0, 712.0, 911.0, 712.0, 943.0, 296.0, 943.0 ], "score": 0.994, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 301.0, 240.0, 613.0, 240.0, 613.0, 278.0, 301.0, 278.0 ], "score": 0.944, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 304.0, 994.0, 1344.0, 994.0, 1344.0, 1028.0, 304.0, 1028.0 ], "score": 0.981, "text": "{{premise}} Based on that information, is the claim: \"{{hypothesis}}\"" }, { "category_id": 15, "poly": [ 305.0, 1022.0, 1014.0, 1022.0, 1014.0, 1057.0, 305.0, 1057.0 ], "score": 0.981, "text": "{{\"true\"}}，{{\"false\"}}，or {{\"inconclusive\"}}?" }, { "category_id": 15, "poly": [ 304.0, 457.0, 1180.0, 457.0, 1180.0, 490.0, 304.0, 490.0 ], "score": 0.986, "text": "{{premise}} Based on the previous passage， is it true that" }, { "category_id": 15, "poly": [ 304.0, 482.0, 851.0, 482.0, 851.0, 520.0, 304.0, 520.0 ], "score": 0.957, "text": "\"{{hypothesis}}\"? Yes, no, or maybe?" }, { "category_id": 15, "poly": [ 299.0, 1497.0, 1346.0, 1497.0, 1346.0, 1538.0, 299.0, 1538.0 ], "score": 0.969, "text": " Given that {{premise}} Does it follow that {{hypothesis}} Yes， no，or" }, { "category_id": 15, "poly": [ 297.0, 1530.0, 403.0, 1530.0, 403.0, 1563.0, 297.0, 1563.0 ], "score": 0.998, "text": "maybe?" }, { "category_id": 15, "poly": [ 306.0, 645.0, 492.0, 645.0, 492.0, 683.0, 306.0, 683.0 ], "score": 0.974, "text": "{% if label " }, { "category_id": 15, "poly": [ 549.0, 645.0, 1161.0, 645.0, 1161.0, 683.0, 549.0, 683.0 ], "score": 0.973, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 1183.0, 497.0, 1183.0, 497.0, 1222.0, 306.0, 1222.0 ], "score": 0.99, "text": "{% if label !" }, { "category_id": 15, "poly": [ 549.0, 1183.0, 1161.0, 1183.0, 1161.0, 1222.0, 549.0, 1222.0 ], "score": 0.967, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 1690.0, 495.0, 1690.0, 495.0, 1728.0, 306.0, 1728.0 ], "score": 0.93, "text": "{% if label ！" }, { "category_id": 15, "poly": [ 549.0, 1690.0, 1161.0, 1690.0, 1161.0, 1728.0, 549.0, 1728.0 ], "score": 0.973, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 372.0, 705.0, 372.0, 705.0, 405.0, 295.0, 405.0 ], "score": 0.984, "text": "Prompt from Schick and Schutze (2021)" }, { "category_id": 15, "poly": [ 295.0, 405.0, 462.0, 405.0, 462.0, 437.0, 295.0, 437.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 302.0, 1317.0, 778.0, 1317.0, 778.0, 1351.0, 302.0, 1351.0 ], "score": 0.97, "text": "True Ill False Ill Inconclusive" }, { "category_id": 15, "poly": [ 300.0, 776.0, 613.0, 776.0, 613.0, 815.0, 300.0, 815.0 ], "score": 0.963, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 300.0, 1821.0, 613.0, 1821.0, 613.0, 1860.0, 300.0, 1860.0 ], "score": 0.963, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 294.0, 721.0, 575.0, 721.0, 575.0, 761.0, 294.0, 761.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1447.0, 463.0, 1447.0, 463.0, 1483.0, 295.0, 1483.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1766.0, 575.0, 1766.0, 575.0, 1806.0, 294.0, 1806.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1258.0, 575.0, 1258.0, 575.0, 1298.0, 294.0, 1298.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 293.0, 1636.0, 474.0, 1636.0, 474.0, 1674.0, 293.0, 1674.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 301.0, 240.0, 613.0, 240.0, 613.0, 278.0, 301.0, 278.0 ], "score": 0.945, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 293.0, 591.0, 474.0, 591.0, 474.0, 629.0, 293.0, 629.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 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"category_id": 15, "poly": [ 294.0, 378.0, 475.0, 378.0, 475.0, 416.0, 294.0, 416.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1487.0, 475.0, 1487.0, 475.0, 1525.0, 294.0, 1525.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1621.0, 575.0, 1621.0, 575.0, 1661.0, 294.0, 1661.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 706.0, 713.0, 706.0, 713.0, 741.0, 295.0, 741.0 ], "score": 0.995, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 740.0, 462.0, 740.0, 462.0, 772.0, 295.0, 772.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1067.0, 575.0, 1067.0, 575.0, 1107.0, 294.0, 1107.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 516.0, 574.0, 516.0, 574.0, 551.0, 295.0, 551.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 303.0, 793.0, 1210.0, 793.0, 1210.0, 832.0, 303.0, 832.0 ], "score": 0.977, "text": "Suppose it's true that {{premise}} Then, is \"{{hypothesis}}\"" }, { "category_id": 15, "poly": [ 306.0, 821.0, 1076.0, 821.0, 1076.0, 860.0, 306.0, 860.0 ], "score": 0.956, "text": "{{\"always\"}}， {{\"sometimes\"}}， or {{\"never\"}} true?" }, { "category_id": 15, "poly": [ 302.0, 237.0, 1358.0, 237.0, 1358.0, 280.0, 302.0, 280.0 ], "score": 0.992, "text": "{{premise}} Are we justified in saying that \"{{hypothesis}}\"? Yes, no," }, { "category_id": 15, "poly": [ 301.0, 271.0, 447.0, 271.0, 447.0, 301.0, 301.0, 301.0 ], "score": 0.989, "text": "or maybe?" }, { "category_id": 15, "poly": [ 306.0, 989.0, 491.0, 989.0, 491.0, 1029.0, 306.0, 1029.0 ], "score": 0.958, "text": "{% if label " }, { "category_id": 15, "poly": [ 549.0, 989.0, 1162.0, 989.0, 1162.0, 1029.0, 549.0, 1029.0 ], "score": 0.974, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 1960.0, 1271.0, 1960.0, 1271.0, 1994.0, 304.0, 1994.0 ], "score": 0.981, "text": "Keeping in mind the above text， consider: {{hypothesis}} Is this" }, { "category_id": 15, "poly": [ 306.0, 1985.0, 1118.0, 1985.0, 1118.0, 2020.0, 306.0, 2020.0 ], "score": 0.972, "text": "{{\"always\"}}，{{\"sometimes\"}}，or {{\"never\"}} correct?" }, { "category_id": 15, "poly": [ 305.0, 435.0, 1162.0, 435.0, 1162.0, 475.0, 305.0, 475.0 ], "score": 0.983, "text": "{% if label !=-1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 516.0, 574.0, 516.0, 574.0, 551.0, 295.0, 551.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1067.0, 575.0, 1067.0, 575.0, 1107.0, 294.0, 1107.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 302.0, 1682.0, 703.0, 1682.0, 703.0, 1716.0, 302.0, 1716.0 ], "score": 0.964, "text": "True IIl False IIl Neither" }, { "category_id": 15, "poly": [ 295.0, 1815.0, 713.0, 1815.0, 713.0, 1849.0, 295.0, 1849.0 ], "score": 0.989, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 1847.0, 462.0, 1847.0, 462.0, 1879.0, 295.0, 1879.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 1543.0, 1162.0, 1543.0, 1162.0, 1583.0, 306.0, 1583.0 ], "score": 0.984, "text": "{% if label !=-1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 1348.0, 477.0, 1348.0, 477.0, 1385.0, 304.0, 1385.0 ], "score": 0.999, "text": "{{premise}}" }, { "category_id": 15, "poly": [ 302.0, 1376.0, 1047.0, 1376.0, 1047.0, 1413.0, 302.0, 1413.0 ], "score": 0.975, "text": "Question: {{hypothesis}} True, False, or Neither?" }, { "category_id": 15, "poly": [ 303.0, 1130.0, 762.0, 1130.0, 762.0, 1162.0, 303.0, 1162.0 ], "score": 0.944, "text": "Always IIl Never IIl Sometimes" }, { "category_id": 15, "poly": [ 300.0, 572.0, 613.0, 572.0, 613.0, 611.0, 300.0, 611.0 ], "score": 0.963, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 295.0, 1258.0, 631.0, 1258.0, 631.0, 1297.0, 295.0, 1297.0 ], "score": 0.983, "text": "Prompt from Brown et al. (2020)" }, { "category_id": 15, "poly": [ 296.0, 1295.0, 463.0, 1295.0, 463.0, 1327.0, 296.0, 1327.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 706.0, 713.0, 706.0, 713.0, 741.0, 295.0, 741.0 ], "score": 0.995, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 740.0, 462.0, 740.0, 462.0, 772.0, 295.0, 772.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1621.0, 575.0, 1621.0, 575.0, 1661.0, 294.0, 1661.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 305.0, 1901.0, 476.0, 1901.0, 476.0, 1939.0, 305.0, 1939.0 ], "score": 0.999, "text": "{{premise}}" }, { "category_id": 15, "poly": [ 294.0, 1487.0, 475.0, 1487.0, 475.0, 1525.0, 294.0, 1525.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 378.0, 475.0, 378.0, 475.0, 416.0, 294.0, 416.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 932.0, 474.0, 932.0, 474.0, 970.0, 293.0, 970.0 ], "score": 0.998, "text": "Target Template:" } ], "page_info": { "page_no": 44, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 300, 685, 1375, 685, 1375, 744, 300, 744 ], "score": 0.908 }, { "category_id": 1, "poly": [ 302, 1217, 1388, 1217, 1388, 1276, 302, 1276 ], "score": 0.898 }, { "category_id": 2, "poly": [ 298, 73, 816, 73, 816, 106, 298, 106 ], "score": 0.865 }, { "category_id": 1, "poly": [ 308, 884, 1158, 884, 1158, 916, 308, 916 ], "score": 0.855 }, { "category_id": 2, "poly": [ 834, 2089, 866, 2089, 866, 2114, 834, 2114 ], "score": 0.844 }, { "category_id": 1, "poly": [ 308, 321, 1158, 321, 1158, 353, 308, 353 ], "score": 0.829 }, { "category_id": 1, "poly": [ 299, 964, 571, 964, 571, 994, 299, 994 ], "score": 0.798 }, { "category_id": 1, "poly": [ 306, 1415, 1158, 1415, 1158, 1447, 306, 1447 ], "score": 0.779 }, { "category_id": 1, "poly": [ 305, 1779, 817, 1779, 817, 1810, 305, 1810 ], "score": 0.762 }, { "category_id": 1, "poly": [ 296, 1688, 713, 1688, 713, 1751, 296, 1751 ], "score": 0.76 }, { "category_id": 1, "poly": [ 306, 461, 759, 461, 759, 493, 306, 493 ], "score": 0.716 }, { "category_id": 1, "poly": [ 296, 595, 712, 595, 712, 656, 296, 656 ], "score": 0.698 }, { "category_id": 0, "poly": [ 297, 1158, 459, 1158, 459, 1188, 297, 1188 ], "score": 0.642 }, { "category_id": 1, "poly": [ 305, 1556, 609, 1556, 609, 1587, 305, 1587 ], "score": 0.642 }, { "category_id": 1, "poly": [ 304, 1023, 609, 1023, 609, 1055, 304, 1055 ], "score": 0.641 }, { "category_id": 1, "poly": [ 298, 1356, 468, 1356, 468, 1386, 298, 1386 ], "score": 0.606 }, { "category_id": 0, "poly": [ 298, 263, 469, 263, 469, 293, 298, 293 ], "score": 0.603 }, { "category_id": 1, "poly": [ 299, 1496, 570, 1496, 570, 1526, 299, 1526 ], "score": 0.576 }, { "category_id": 1, "poly": [ 299, 403, 570, 403, 570, 433, 299, 433 ], "score": 0.555 }, { "category_id": 1, "poly": [ 298, 825, 469, 825, 469, 855, 298, 855 ], "score": 0.504 }, { "category_id": 1, "poly": [ 300, 1834, 1313, 1834, 1313, 1892, 300, 1892 ], "score": 0.497 }, { "category_id": 0, "poly": [ 298, 825, 469, 825, 469, 855, 298, 855 ], "score": 0.455 }, { "category_id": 0, "poly": [ 298, 1974, 469, 1974, 469, 2005, 298, 2005 ], "score": 0.445 }, { "category_id": 1, "poly": [ 298, 1974, 469, 1974, 469, 2005, 298, 2005 ], "score": 0.437 }, { "category_id": 0, "poly": [ 299, 403, 570, 403, 570, 433, 299, 433 ], "score": 0.404 }, { "category_id": 0, "poly": [ 299, 1496, 570, 1496, 570, 1526, 299, 1526 ], "score": 0.401 }, { "category_id": 0, "poly": [ 298, 1356, 468, 1356, 468, 1386, 298, 1386 ], "score": 0.335 }, { "category_id": 1, "poly": [ 298, 263, 469, 263, 469, 293, 298, 293 ], "score": 0.321 }, { "category_id": 1, "poly": [ 297, 1158, 459, 1158, 459, 1188, 297, 1188 ], "score": 0.254 }, { "category_id": 0, "poly": [ 299, 964, 571, 964, 571, 994, 299, 994 ], "score": 0.131 }, { "category_id": 13, "poly": [ 489, 324, 548, 324, 548, 350, 489, 350 ], "score": 0.35, "latex": "\\ ! = - 1" }, { "category_id": 13, "poly": [ 496, 886, 548, 886, 548, 912, 496, 912 ], "score": 0.34, "latex": "\\ c = - 1" }, { "category_id": 13, "poly": [ 496, 1417, 548, 1417, 548, 1443, 496, 1443 ], "score": 0.28, "latex": "\\ c = - 1" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.993, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2085.0, 871.0, 2085.0, 871.0, 2122.0, 829.0, 2122.0 ], "score": 1.0, "text": "46" }, { "category_id": 15, "poly": [ 295.0, 1156.0, 463.0, 1156.0, 463.0, 1192.0, 295.0, 1192.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 260.0, 475.0, 260.0, 475.0, 298.0, 294.0, 298.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 822.0, 475.0, 822.0, 475.0, 860.0, 294.0, 860.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1971.0, 475.0, 1971.0, 475.0, 2010.0, 294.0, 2010.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 398.0, 575.0, 398.0, 575.0, 438.0, 294.0, 438.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1491.0, 575.0, 1491.0, 575.0, 1531.0, 294.0, 1531.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 293.0, 1353.0, 474.0, 1353.0, 474.0, 1391.0, 293.0, 1391.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 962.0, 574.0, 962.0, 574.0, 997.0, 295.0, 997.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 299.0, 680.0, 1375.0, 680.0, 1375.0, 721.0, 299.0, 721.0 ], "score": 0.984, "text": " Given {{premise}} Is it guaranteed true that \"{{hypothesis}}\"? Yes， no," }, { "category_id": 15, "poly": [ 300.0, 713.0, 447.0, 713.0, 447.0, 743.0, 300.0, 743.0 ], "score": 0.947, "text": "or maybe?" }, { "category_id": 15, "poly": [ 301.0, 1214.0, 1391.0, 1214.0, 1391.0, 1249.0, 301.0, 1249.0 ], "score": 0.984, "text": "Given that {{premise}} Therefore, it must be true that \"{{hypothesis}}\"?" }, { "category_id": 15, "poly": [ 300.0, 1243.0, 582.0, 1243.0, 582.0, 1278.0, 300.0, 1278.0 ], "score": 0.905, "text": " Yes， no, or maybe?" }, { "category_id": 15, "poly": [ 306.0, 882.0, 495.0, 882.0, 495.0, 918.0, 306.0, 918.0 ], "score": 0.925, "text": "{% if label !" }, { "category_id": 15, "poly": [ 549.0, 882.0, 1161.0, 882.0, 1161.0, 918.0, 549.0, 918.0 ], "score": 0.964, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 317.0, 488.0, 317.0, 488.0, 357.0, 305.0, 357.0 ], "score": 0.911, "text": "{% if label " }, { "category_id": 15, "poly": [ 549.0, 317.0, 1162.0, 317.0, 1162.0, 357.0, 549.0, 357.0 ], "score": 0.976, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 962.0, 574.0, 962.0, 574.0, 997.0, 295.0, 997.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 306.0, 1413.0, 495.0, 1413.0, 495.0, 1449.0, 306.0, 1449.0 ], "score": 0.925, "text": "{% if label !" }, { "category_id": 15, "poly": [ 549.0, 1413.0, 1160.0, 1413.0, 1160.0, 1449.0, 549.0, 1449.0 ], "score": 0.962, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 301.0, 1776.0, 819.0, 1776.0, 819.0, 1812.0, 301.0, 1812.0 ], "score": 0.995, "text": "Assume it is true that {{premise}}" }, { "category_id": 15, "poly": [ 295.0, 1686.0, 713.0, 1686.0, 713.0, 1722.0, 295.0, 1722.0 ], "score": 0.992, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 294.0, 1720.0, 462.0, 1720.0, 462.0, 1752.0, 294.0, 1752.0 ], "score": 0.993, "text": "Input Template:" }, { "category_id": 15, "poly": [ 302.0, 459.0, 763.0, 459.0, 763.0, 495.0, 302.0, 495.0 ], "score": 0.958, "text": "Always IIl Never IIl Sometimes" }, { "category_id": 15, "poly": [ 294.0, 594.0, 713.0, 594.0, 713.0, 628.0, 294.0, 628.0 ], "score": 0.992, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 627.0, 463.0, 627.0, 463.0, 659.0, 295.0, 659.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 303.0, 1555.0, 611.0, 1555.0, 611.0, 1588.0, 303.0, 1588.0 ], "score": 0.931, "text": "Yes III No III Maybe" }, { "category_id": 15, "poly": [ 301.0, 1019.0, 613.0, 1019.0, 613.0, 1058.0, 301.0, 1058.0 ], "score": 0.976, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 293.0, 1353.0, 474.0, 1353.0, 474.0, 1391.0, 293.0, 1391.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1491.0, 575.0, 1491.0, 575.0, 1531.0, 294.0, 1531.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 398.0, 575.0, 398.0, 575.0, 438.0, 294.0, 438.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 822.0, 475.0, 822.0, 475.0, 860.0, 294.0, 860.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 301.0, 1828.0, 1318.0, 1828.0, 1318.0, 1869.0, 301.0, 1869.0 ], "score": 0.967, "text": "Therefore，\"{{hypothesis}}\" is {{\"guaranteed\"}}， {{\"possible\"}}， or" }, { "category_id": 15, "poly": [ 307.0, 1862.0, 564.0, 1862.0, 564.0, 1891.0, 307.0, 1891.0 ], "score": 0.996, "text": "{{\"impossible\"}}?" }, { "category_id": 15, "poly": [ 294.0, 1971.0, 475.0, 1971.0, 475.0, 2010.0, 294.0, 2010.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 260.0, 475.0, 260.0, 475.0, 298.0, 294.0, 298.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1156.0, 463.0, 1156.0, 463.0, 1192.0, 295.0, 1192.0 ], "score": 0.999, "text": "Input Template:" } ], "page_info": { "page_no": 45, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 303, 1139, 1388, 1139, 1388, 1226, 303, 1226 ], "score": 0.911 }, { "category_id": 1, "poly": [ 299, 1712, 1357, 1712, 1357, 1774, 299, 1774 ], "score": 0.871 }, { "category_id": 2, "poly": [ 834, 2088, 864, 2088, 864, 2113, 834, 2113 ], "score": 0.852 }, { "category_id": 2, "poly": [ 298, 73, 816, 73, 816, 106, 298, 106 ], "score": 0.842 }, { "category_id": 1, "poly": [ 303, 377, 879, 377, 879, 409, 303, 409 ], "score": 0.825 }, { "category_id": 1, "poly": [ 298, 1984, 571, 1984, 571, 2016, 298, 2016 ], "score": 0.806 }, { "category_id": 1, "poly": [ 307, 1495, 876, 1495, 876, 1527, 307, 1527 ], "score": 0.803 }, { "category_id": 1, "poly": [ 306, 786, 1129, 786, 1129, 817, 306, 817 ], "score": 0.786 }, { "category_id": 1, "poly": [ 307, 242, 1158, 242, 1158, 275, 307, 275 ], "score": 0.781 }, { "category_id": 1, "poly": [ 299, 1439, 571, 1439, 571, 1469, 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"poly": [ 293.0, 1301.0, 474.0, 1301.0, 474.0, 1339.0, 293.0, 1339.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 316.0, 575.0, 316.0, 575.0, 356.0, 294.0, 356.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 863.0, 574.0, 863.0, 574.0, 898.0, 295.0, 898.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1437.0, 574.0, 1437.0, 574.0, 1472.0, 295.0, 1472.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1625.0, 712.0, 1625.0, 712.0, 1659.0, 295.0, 1659.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 296.0, 1658.0, 463.0, 1658.0, 463.0, 1690.0, 296.0, 1690.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1049.0, 655.0, 1049.0, 655.0, 1086.0, 295.0, 1086.0 ], "score": 0.996, "text": "Prompt from Williams et al. (2018)" }, { "category_id": 15, "poly": [ 295.0, 1084.0, 462.0, 1084.0, 462.0, 1116.0, 295.0, 1116.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 1137.0, 1391.0, 1137.0, 1391.0, 1173.0, 306.0, 1173.0 ], "score": 0.998, "text": "{{premise}} Using only the above description and what you know about the" }, { "category_id": 15, "poly": [ 301.0, 1167.0, 1212.0, 1167.0, 1212.0, 1200.0, 301.0, 1200.0 ], "score": 0.97, "text": "world,\"{{hypothesis}}\" is definitely correct， incorrect，or" }, { "category_id": 15, "poly": [ 304.0, 1195.0, 506.0, 1195.0, 506.0, 1226.0, 304.0, 1226.0 ], "score": 1.0, "text": "inconclusive?" }, { "category_id": 15, "poly": [ 298.0, 1708.0, 1358.0, 1708.0, 1358.0, 1748.0, 298.0, 1748.0 ], "score": 0.98, "text": " Given {{premise}} Should we assume that \"{{hypothesis}}\" is true? Yes," }, { "category_id": 15, "poly": [ 298.0, 1741.0, 507.0, 1741.0, 507.0, 1773.0, 298.0, 1773.0 ], "score": 0.969, "text": " no，or maybe?" }, { "category_id": 15, "poly": [ 301.0, 375.0, 883.0, 375.0, 883.0, 410.0, 301.0, 410.0 ], "score": 0.973, "text": "Guaranteed Ill Impossible IIl Possible" }, { "category_id": 15, "poly": [ 295.0, 1979.0, 574.0, 1979.0, 574.0, 2021.0, 295.0, 2021.0 ], "score": 0.97, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 1493.0, 880.0, 1493.0, 880.0, 1528.0, 304.0, 1528.0 ], "score": 0.96, "text": "Correct IIl Incorrect IIl Inconclusive" }, { "category_id": 15, "poly": [ 307.0, 784.0, 497.0, 784.0, 497.0, 819.0, 307.0, 819.0 ], "score": 0.961, "text": "{% if label !" }, { "category_id": 15, "poly": [ 549.0, 784.0, 1132.0, 784.0, 1132.0, 819.0, 549.0, 819.0 ], "score": 0.992, "text": "%}{{answer_choices[label]}}{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 239.0, 1161.0, 239.0, 1161.0, 278.0, 306.0, 278.0 ], "score": 0.98, "text": "{% if label !=-1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 1437.0, 574.0, 1437.0, 574.0, 1472.0, 295.0, 1472.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 306.0, 1903.0, 489.0, 1903.0, 489.0, 1942.0, 306.0, 1942.0 ], "score": 0.951, "text": "{% if label " }, { "category_id": 15, "poly": [ 549.0, 1903.0, 1161.0, 1903.0, 1161.0, 1942.0, 549.0, 1942.0 ], "score": 0.967, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 307.0, 1358.0, 1160.0, 1358.0, 1160.0, 1394.0, 307.0, 1394.0 ], "score": 0.981, "text": "{% if label !=-1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 300.0, 917.0, 613.0, 917.0, 613.0, 956.0, 300.0, 956.0 ], "score": 0.963, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 295.0, 863.0, 574.0, 863.0, 574.0, 898.0, 295.0, 898.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 316.0, 575.0, 316.0, 575.0, 356.0, 294.0, 356.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1049.0, 655.0, 1049.0, 655.0, 1086.0, 295.0, 1086.0 ], "score": 0.996, "text": "Prompt from Williams et al. (2018)" }, { "category_id": 15, "poly": [ 295.0, 1084.0, 462.0, 1084.0, 462.0, 1116.0, 295.0, 1116.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 727.0, 475.0, 727.0, 475.0, 765.0, 294.0, 765.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1301.0, 474.0, 1301.0, 474.0, 1339.0, 293.0, 1339.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 301.0, 620.0, 1315.0, 620.0, 1315.0, 655.0, 301.0, 655.0 ], "score": 0.978, "text": "Question: Does this imply that \"{{hypothesis}}\"? Yes, no, or maybe?" }, { "category_id": 15, "poly": [ 295.0, 1625.0, 712.0, 1625.0, 712.0, 1659.0, 295.0, 1659.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 296.0, 1658.0, 463.0, 1658.0, 463.0, 1690.0, 296.0, 1690.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1848.0, 475.0, 1848.0, 475.0, 1886.0, 294.0, 1886.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 507.0, 464.0, 507.0, 464.0, 543.0, 295.0, 543.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 561.0, 476.0, 561.0, 476.0, 599.0, 305.0, 599.0 ], "score": 0.999, "text": "{{premise}}" } ], "page_info": { "page_no": 46, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 298, 1234, 1237, 1234, 1237, 1407, 298, 1407 ], "score": 0.97, "html": "
KeyValue
hypothesisWeapons of Mass Destruction Found in Iraq.
idx0
label1
premise No Weapons of Mass Destruction Found in Iraq Yet.
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1" }, { "category_id": 15, "poly": [ 297.0, 1021.0, 595.0, 1021.0, 595.0, 1053.0, 297.0, 1053.0 ], "score": 0.977, "text": "1.3.2SUPER_GLUE RTE" }, { "category_id": 15, "poly": [ 292.0, 1466.0, 408.0, 1466.0, 408.0, 1508.0, 292.0, 1508.0 ], "score": 1.0, "text": "Prompts" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 1780.0, 474.0, 1780.0, 474.0, 1818.0, 293.0, 1818.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 870.0, 2085.0, 870.0, 2121.0, 830.0, 2121.0 ], "score": 1.0, "text": "48" }, { "category_id": 15, "poly": [ 293.0, 638.0, 474.0, 638.0, 474.0, 676.0, 293.0, 676.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1142.0, 473.0, 1142.0, 473.0, 1181.0, 294.0, 1181.0 ], "score": 0.999, "text": "Data Example" }, { "category_id": 15, "poly": [ 294.0, 781.0, 575.0, 781.0, 575.0, 821.0, 294.0, 821.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1922.0, 575.0, 1922.0, 575.0, 1962.0, 294.0, 1962.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 300.0, 1984.0, 464.0, 1984.0, 464.0, 2022.0, 300.0, 2022.0 ], "score": 0.877, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 295.0, 378.0, 712.0, 378.0, 712.0, 412.0, 295.0, 412.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 411.0, 462.0, 411.0, 462.0, 443.0, 295.0, 443.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 870.0, 2085.0, 870.0, 2121.0, 830.0, 2121.0 ], "score": 1.0, "text": "48" }, { "category_id": 15, "poly": [ 294.0, 1142.0, 473.0, 1142.0, 473.0, 1181.0, 294.0, 1181.0 ], "score": 0.999, "text": "Data Example" }, { "category_id": 15, "poly": [ 301.0, 239.0, 613.0, 239.0, 613.0, 278.0, 301.0, 278.0 ], "score": 0.976, "text": "Yes IIl No IIl Maybe" }, { "category_id": 15, "poly": [ 305.0, 473.0, 907.0, 473.0, 907.0, 502.0, 305.0, 502.0 ], "score": 0.98, "text": "Take the following as truth: {{premise}}" }, { "category_id": 15, "poly": [ 304.0, 498.0, 1215.0, 498.0, 1215.0, 531.0, 304.0, 531.0 ], "score": 0.983, "text": "Then the following statement: \"{{hypothesis}}\" is {{\"true\"}}," }, { "category_id": 15, "poly": [ 307.0, 528.0, 541.0, 528.0, 541.0, 558.0, 307.0, 558.0 ], "score": 0.98, "text": "{{\"false\"}}，or" }, { "category_id": 15, "poly": [ 543.0, 526.0, 836.0, 526.0, 836.0, 558.0, 543.0, 558.0 ], "score": 0.996, "text": "{{\"inconclusive\"}}?" }, { "category_id": 15, "poly": [ 294.0, 1071.0, 896.0, 1071.0, 896.0, 1112.0, 294.0, 1112.0 ], "score": 0.967, "text": "Dataset from Dagan et al. (2005). Used in evaluation." }, { "category_id": 15, "poly": [ 304.0, 1638.0, 1392.0, 1638.0, 1392.0, 1673.0, 304.0, 1673.0 ], "score": 0.995, "text": "{{premise}} Using only the above description and what you know about the" }, { "category_id": 15, "poly": [ 299.0, 1664.0, 1166.0, 1664.0, 1166.0, 1703.0, 299.0, 1703.0 ], "score": 0.983, "text": " world, is \"{{hypothesis}}\" definitely correct? Yes or no?" }, { "category_id": 15, "poly": [ 304.0, 1840.0, 498.0, 1840.0, 498.0, 1878.0, 304.0, 1878.0 ], "score": 0.971, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1840.0, 1178.0, 1840.0, 1178.0, 1878.0, 524.0, 1878.0 ], "score": 0.969, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 307.0, 701.0, 495.0, 701.0, 495.0, 736.0, 307.0, 736.0 ], "score": 0.916, "text": "{% if label !" }, { "category_id": 15, "poly": [ 549.0, 701.0, 1161.0, 701.0, 1161.0, 736.0, 549.0, 736.0 ], "score": 0.978, "text": "%}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 302.0, 846.0, 779.0, 846.0, 779.0, 880.0, 302.0, 880.0 ], "score": 0.966, "text": "True Ill False Ill Inconclusive" }, { "category_id": 15, "poly": [ 295.0, 378.0, 712.0, 378.0, 712.0, 412.0, 295.0, 412.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 411.0, 462.0, 411.0, 462.0, 443.0, 295.0, 443.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1922.0, 575.0, 1922.0, 575.0, 1962.0, 294.0, 1962.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 781.0, 575.0, 781.0, 575.0, 821.0, 294.0, 821.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 296.0, 1546.0, 656.0, 1546.0, 656.0, 1581.0, 296.0, 1581.0 ], "score": 0.995, "text": "Prompt from Williams et al. 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= ~ - 1" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.993, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2085.0, 871.0, 2085.0, 871.0, 2120.0, 829.0, 2120.0 ], "score": 1.0, "text": "49" }, { "category_id": 15, "poly": [ 293.0, 1630.0, 474.0, 1630.0, 474.0, 1668.0, 293.0, 1668.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1058.0, 475.0, 1058.0, 475.0, 1096.0, 294.0, 1096.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 515.0, 475.0, 515.0, 475.0, 553.0, 294.0, 553.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1771.0, 574.0, 1771.0, 574.0, 1811.0, 295.0, 1811.0 ], "score": 0.99, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1202.0, 574.0, 1202.0, 574.0, 1237.0, 295.0, 1237.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1970.0, 463.0, 1970.0, 463.0, 2006.0, 295.0, 2006.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 300.0, 1262.0, 464.0, 1262.0, 464.0, 1297.0, 300.0, 1297.0 ], "score": 0.917, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 300.0, 719.0, 464.0, 719.0, 464.0, 754.0, 300.0, 754.0 ], "score": 0.917, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 295.0, 658.0, 574.0, 658.0, 574.0, 693.0, 295.0, 693.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 299.0, 370.0, 1348.0, 370.0, 1348.0, 411.0, 299.0, 411.0 ], "score": 0.99, "text": " Given {{premise}} Is it guaranteed true that \"{{hypothesis}}\"? Yes or" }, { "category_id": 15, "poly": [ 300.0, 406.0, 357.0, 406.0, 357.0, 434.0, 300.0, 434.0 ], "score": 0.879, "text": "no?" }, { "category_id": 15, "poly": [ 305.0, 1491.0, 478.0, 1491.0, 478.0, 1525.0, 305.0, 1525.0 ], "score": 1.0, "text": "{{premise}}" }, { "category_id": 15, "poly": [ 299.0, 1508.0, 900.0, 1508.0, 900.0, 1557.0, 299.0, 1557.0 ], "score": 0.97, "text": "Question: {{hypothesis}} True or False?" }, { "category_id": 15, "poly": [ 304.0, 1690.0, 498.0, 1690.0, 498.0, 1728.0, 304.0, 1728.0 ], "score": 0.975, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1690.0, 1179.0, 1690.0, 1179.0, 1728.0, 524.0, 1728.0 ], "score": 0.967, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 1119.0, 490.0, 1119.0, 490.0, 1157.0, 305.0, 1157.0 ], "score": 0.967, "text": "{% if label " }, { "category_id": 15, "poly": [ 565.0, 1119.0, 1179.0, 1119.0, 1179.0, 1157.0, 565.0, 1157.0 ], "score": 0.975, "text": " %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 574.0, 499.0, 574.0, 499.0, 614.0, 305.0, 614.0 ], "score": 0.955, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 574.0, 1178.0, 574.0, 1178.0, 614.0, 524.0, 614.0 ], "score": 0.968, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 658.0, 574.0, 658.0, 574.0, 693.0, 295.0, 693.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 299.0, 947.0, 1305.0, 947.0, 1305.0, 982.0, 299.0, 982.0 ], "score": 0.975, "text": " Suppose {{premise}} Can we infer that \"{{hypothesis}}\"? Yes or no?" }, { "category_id": 15, "poly": [ 295.0, 1397.0, 631.0, 1397.0, 631.0, 1435.0, 295.0, 1435.0 ], "score": 0.998, "text": "Prompt from Brown et al. (2020)" }, { "category_id": 15, "poly": [ 296.0, 1432.0, 462.0, 1432.0, 462.0, 1464.0, 296.0, 1464.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 284.0, 712.0, 284.0, 712.0, 318.0, 295.0, 318.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 315.0, 464.0, 315.0, 464.0, 350.0, 295.0, 350.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1202.0, 574.0, 1202.0, 574.0, 1237.0, 295.0, 1237.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 855.0, 712.0, 855.0, 712.0, 889.0, 295.0, 889.0 ], "score": 0.987, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 296.0, 888.0, 463.0, 888.0, 463.0, 920.0, 296.0, 920.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1771.0, 574.0, 1771.0, 574.0, 1811.0, 295.0, 1811.0 ], "score": 0.983, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 302.0, 1835.0, 522.0, 1835.0, 522.0, 1868.0, 302.0, 1868.0 ], "score": 0.965, "text": "True IIl False" }, { "category_id": 15, "poly": [ 295.0, 1970.0, 463.0, 1970.0, 463.0, 2006.0, 295.0, 2006.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 515.0, 475.0, 515.0, 475.0, 553.0, 294.0, 553.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1058.0, 475.0, 1058.0, 475.0, 1096.0, 294.0, 1096.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1630.0, 474.0, 1630.0, 474.0, 1668.0, 293.0, 1668.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 300.0, 719.0, 464.0, 719.0, 464.0, 754.0, 300.0, 754.0 ], "score": 0.917, "text": "Yes IIl No" } ], "page_info": { "page_no": 48, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 296, 815, 1390, 815, 1390, 875, 296, 875 ], 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"category_id": 0, "poly": [ 299, 729, 712, 729, 712, 759, 299, 759 ], "score": 0.113 }, { "category_id": 1, "poly": [ 305, 1144, 460, 1144, 460, 1174, 305, 1174 ], "score": 0.096 }, { "category_id": 13, "poly": [ 499, 1500, 523, 1500, 523, 1521, 499, 1521 ], "score": 0.43, "latex": "=" }, { "category_id": 13, "poly": [ 500, 1013, 523, 1013, 523, 1034, 500, 1034 ], "score": 0.41, "latex": "=" }, { "category_id": 13, "poly": [ 498, 467, 523, 467, 523, 488, 498, 488 ], "score": 0.4, "latex": "=" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.993, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2085.0, 871.0, 2085.0, 871.0, 2123.0, 829.0, 2123.0 ], "score": 1.0, "text": "50" }, { "category_id": 15, "poly": [ 294.0, 950.0, 475.0, 950.0, 475.0, 988.0, 294.0, 988.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1273.0, 463.0, 1273.0, 463.0, 1309.0, 295.0, 1309.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 404.0, 475.0, 404.0, 475.0, 442.0, 294.0, 442.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1982.0, 475.0, 1982.0, 475.0, 2021.0, 294.0, 2021.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1437.0, 474.0, 1437.0, 474.0, 1475.0, 293.0, 1475.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1085.0, 574.0, 1085.0, 574.0, 1120.0, 295.0, 1120.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 539.0, 574.0, 539.0, 574.0, 574.0, 295.0, 574.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 301.0, 1141.0, 465.0, 1141.0, 465.0, 1176.0, 301.0, 1176.0 ], "score": 0.91, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 300.0, 1628.0, 464.0, 1628.0, 464.0, 1663.0, 300.0, 1663.0 ], "score": 0.917, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 301.0, 595.0, 465.0, 595.0, 465.0, 630.0, 301.0, 630.0 ], "score": 0.91, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 294.0, 1569.0, 575.0, 1569.0, 575.0, 1609.0, 294.0, 1609.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 296.0, 729.0, 712.0, 729.0, 712.0, 761.0, 296.0, 761.0 ], "score": 0.994, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 299.0, 814.0, 1393.0, 814.0, 1393.0, 849.0, 299.0, 849.0 ], "score": 0.993, "text": " Given {{premise}} Should we assume that \"{{hypothesis}}\" is true? Yes or" }, { "category_id": 15, "poly": [ 301.0, 844.0, 357.0, 844.0, 357.0, 874.0, 301.0, 874.0 ], "score": 0.851, "text": "no？" }, { "category_id": 15, "poly": [ 304.0, 1846.0, 1181.0, 1846.0, 1181.0, 1881.0, 304.0, 1881.0 ], "score": 0.984, "text": "{{premise}} Based on the previous passage， is it true that" }, { "category_id": 15, "poly": [ 304.0, 1873.0, 732.0, 1873.0, 732.0, 1907.0, 304.0, 1907.0 ], "score": 0.987, "text": "\"{{hypothesis}}\"? Yes or no?" }, { "category_id": 15, "poly": [ 304.0, 460.0, 497.0, 460.0, 497.0, 498.0, 304.0, 498.0 ], "score": 0.974, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 460.0, 1149.0, 460.0, 1149.0, 498.0, 524.0, 498.0 ], "score": 0.974, "text": " -1 %}{{answer_choices[label]}}{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 1005.0, 499.0, 1005.0, 499.0, 1043.0, 305.0, 1043.0 ], "score": 0.963, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1005.0, 1177.0, 1005.0, 1177.0, 1043.0, 524.0, 1043.0 ], "score": 0.963, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 301.0, 297.0, 1197.0, 297.0, 1197.0, 332.0, 301.0, 332.0 ], "score": 0.988, "text": " Question: Does this imply that \"{{hypothesis}}\"? 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KeyValue
hypothesis labelThe trolleybus system has over 2 urban routes 0
premiseThe Parma trolleybus system (Italian: "Rete filovi...
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Yes IIl No
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Yes IIl No
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712.0, 1841.0, 712.0, 1875.0, 295.0, 1875.0 ], "score": 0.993, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 1875.0, 463.0, 1875.0, 463.0, 1907.0, 295.0, 1907.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1081.0, 575.0, 1081.0, 575.0, 1121.0, 295.0, 1121.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1648.0, 574.0, 1648.0, 574.0, 1683.0, 295.0, 1683.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 303.0, 1002.0, 716.0, 1002.0, 716.0, 1042.0, 303.0, 1042.0 ], "score": 0.961, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 297.0, 1701.0, 615.0, 1701.0, 615.0, 1747.0, 297.0, 1747.0 ], "score": 0.911, "text": " Yes IIl Maybe II No" }, { "category_id": 15, "poly": [ 305.0, 1934.0, 906.0, 1934.0, 906.0, 1963.0, 305.0, 1963.0 ], "score": 0.969, "text": "Take the following as truth: {{premise}}" }, { "category_id": 15, "poly": [ 304.0, 1960.0, 1217.0, 1960.0, 1217.0, 1993.0, 304.0, 1993.0 ], "score": 0.991, "text": "Then the following statement: \"{{hypothesis}}\" is {{\"true\"}}," }, { "category_id": 15, "poly": [ 307.0, 1985.0, 837.0, 1985.0, 837.0, 2021.0, 307.0, 2021.0 ], "score": 0.975, "text": "{{\"false\"}}，or {{\"inconclusive\"}}?" }, { "category_id": 15, "poly": [ 302.0, 1363.0, 1359.0, 1363.0, 1359.0, 1406.0, 302.0, 1406.0 ], "score": 0.987, "text": "{{premise}} Are we justified in saying that \"{{hypothesis}}\"? Yes， no," }, { "category_id": 15, "poly": [ 300.0, 1398.0, 447.0, 1398.0, 447.0, 1428.0, 300.0, 1428.0 ], "score": 0.945, "text": "or maybe?" }, { "category_id": 15, "poly": [ 303.0, 804.0, 1181.0, 804.0, 1181.0, 839.0, 303.0, 839.0 ], "score": 0.97, "text": "{{premise}} Based on the previous passage， is it true that" }, { "category_id": 15, "poly": [ 303.0, 829.0, 853.0, 829.0, 853.0, 869.0, 303.0, 869.0 ], "score": 0.949, "text": "\"{{hypothesis}}\"? Yes, no, or maybe?" }, { "category_id": 15, "poly": [ 307.0, 1568.0, 713.0, 1568.0, 713.0, 1601.0, 307.0, 1601.0 ], "score": 0.967, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 307.0, 442.0, 713.0, 442.0, 713.0, 475.0, 307.0, 475.0 ], "score": 0.967, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 306.0, 1003.0, 714.0, 1003.0, 714.0, 1040.0, 306.0, 1040.0 ], "score": 0.981, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 294.0, 712.0, 706.0, 712.0, 706.0, 751.0, 294.0, 751.0 ], "score": 0.976, "text": "Prompt from Schick and Schutze (2021)" }, { "category_id": 15, "poly": [ 296.0, 748.0, 462.0, 748.0, 462.0, 780.0, 296.0, 780.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 240.0, 479.0, 240.0, 479.0, 278.0, 304.0, 278.0 ], "score": 0.999, "text": "{{premise}}" }, { "category_id": 15, "poly": [ 296.0, 257.0, 1052.0, 257.0, 1052.0, 309.0, 296.0, 309.0 ], "score": 0.966, "text": "Question: {(hypothesis}} True, False, or Neither?" }, { "category_id": 15, "poly": [ 295.0, 1648.0, 574.0, 1648.0, 574.0, 1683.0, 295.0, 1683.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1279.0, 713.0, 1279.0, 713.0, 1312.0, 295.0, 1312.0 ], "score": 0.992, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 293.0, 1310.0, 463.0, 1310.0, 463.0, 1343.0, 293.0, 1343.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 303.0, 1145.0, 612.0, 1145.0, 612.0, 1178.0, 303.0, 1178.0 ], "score": 0.915, "text": "Yes III Maybe III No" }, { "category_id": 15, "poly": [ 295.0, 1081.0, 575.0, 1081.0, 575.0, 1121.0, 295.0, 1121.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 302.0, 580.0, 703.0, 580.0, 703.0, 614.0, 302.0, 614.0 ], "score": 0.943, "text": "True IIl Neither IIl False" }, { "category_id": 15, "poly": [ 301.0, 1704.0, 614.0, 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575.0, 438.0, 294.0, 438.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1971.0, 475.0, 1971.0, 475.0, 2010.0, 294.0, 2010.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 594.0, 463.0, 594.0, 463.0, 630.0, 294.0, 630.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 929.0, 575.0, 929.0, 575.0, 969.0, 294.0, 969.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1353.0, 475.0, 1353.0, 475.0, 1391.0, 294.0, 1391.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 297.0, 986.0, 616.0, 986.0, 616.0, 1032.0, 297.0, 1032.0 ], "score": 0.933, "text": "Yes IIl Maybe III No" }, { "category_id": 15, "poly": [ 302.0, 1411.0, 714.0, 1411.0, 714.0, 1451.0, 302.0, 1451.0 ], "score": 0.949, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 298.0, 1550.0, 615.0, 1550.0, 615.0, 1593.0, 298.0, 1593.0 ], "score": 0.923, "text": " Yes IIl Maybe III No" }, { "category_id": 15, "poly": [ 303.0, 652.0, 1390.0, 652.0, 1390.0, 686.0, 303.0, 686.0 ], "score": 0.98, "text": "Given that {{premise}} Therefore, it must be true that \"{{hypothesis}}\"?" }, { "category_id": 15, "poly": [ 301.0, 682.0, 582.0, 682.0, 582.0, 716.0, 301.0, 716.0 ], "score": 0.909, "text": " Yes， no， or maybe?" }, { "category_id": 15, "poly": [ 300.0, 1213.0, 1315.0, 1213.0, 1315.0, 1251.0, 300.0, 1251.0 ], "score": 0.96, "text": " Suppose {{premise}} Can we infer that \"{{hypothesis}}\"? Yes， no， or" }, { "category_id": 15, "poly": [ 297.0, 1243.0, 405.0, 1243.0, 405.0, 1277.0, 297.0, 1277.0 ], "score": 0.994, "text": "maybe?" }, { "category_id": 15, "poly": [ 307.0, 853.0, 713.0, 853.0, 713.0, 886.0, 307.0, 886.0 ], "score": 0.965, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 305.0, 320.0, 713.0, 320.0, 713.0, 357.0, 305.0, 357.0 ], "score": 0.971, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 306.0, 1415.0, 713.0, 1415.0, 713.0, 1448.0, 306.0, 1448.0 ], "score": 0.98, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 295.0, 1687.0, 713.0, 1687.0, 713.0, 1721.0, 295.0, 1721.0 ], "score": 0.989, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 1719.0, 462.0, 1719.0, 462.0, 1751.0, 295.0, 1751.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 303.0, 459.0, 778.0, 459.0, 778.0, 494.0, 303.0, 494.0 ], "score": 0.961, "text": "True Ill Inconclusive IIl False" }, { "category_id": 15, "poly": [ 294.0, 929.0, 575.0, 929.0, 575.0, 969.0, 294.0, 969.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 302.0, 1779.0, 819.0, 1779.0, 819.0, 1811.0, 302.0, 1811.0 ], "score": 0.999, "text": "Assume it is true that {{premise}}" }, { "category_id": 15, "poly": [ 294.0, 1353.0, 475.0, 1353.0, 475.0, 1391.0, 294.0, 1391.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 303.0, 1555.0, 612.0, 1555.0, 612.0, 1588.0, 303.0, 1588.0 ], "score": 0.892, "text": "Yes III Maybe III No" }, { "category_id": 15, "poly": [ 301.0, 989.0, 614.0, 989.0, 614.0, 1028.0, 301.0, 1028.0 ], "score": 0.967, "text": "Yes IIl Maybe IIl No" }, { "category_id": 15, "poly": [ 294.0, 398.0, 575.0, 398.0, 575.0, 438.0, 294.0, 438.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 594.0, 463.0, 594.0, 463.0, 630.0, 294.0, 630.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1491.0, 575.0, 1491.0, 575.0, 1531.0, 294.0, 1531.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 301.0, 1828.0, 1318.0, 1828.0, 1318.0, 1869.0, 301.0, 1869.0 ], "score": 0.967, "text": "Therefore，\"{{hypothesis}}\" is {{\"guaranteed\"}}， {{\"possible\"}}， or" }, { "category_id": 15, "poly": [ 304.0, 1861.0, 566.0, 1861.0, 566.0, 1894.0, 304.0, 1894.0 ], "score": 0.995, "text": "{{\"impossible\"}}?" }, { "category_id": 15, "poly": [ 294.0, 1971.0, 475.0, 1971.0, 475.0, 2010.0, 294.0, 2010.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 792.0, 474.0, 792.0, 474.0, 830.0, 293.0, 830.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 260.0, 475.0, 260.0, 475.0, 298.0, 294.0, 298.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1125.0, 712.0, 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"score": 0.09 }, { "category_id": 15, "poly": [ 294.0, 1926.0, 475.0, 1926.0, 475.0, 1964.0, 294.0, 1964.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 870.0, 2085.0, 870.0, 2123.0, 830.0, 2123.0 ], "score": 1.0, "text": "55" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.993, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 295.0, 1080.0, 464.0, 1080.0, 464.0, 1116.0, 295.0, 1116.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 319.0, 575.0, 319.0, 575.0, 359.0, 294.0, 359.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 745.0, 475.0, 745.0, 475.0, 783.0, 294.0, 783.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1306.0, 474.0, 1306.0, 474.0, 1344.0, 293.0, 1344.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 883.0, 575.0, 883.0, 575.0, 923.0, 294.0, 923.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 305.0, 1729.0, 476.0, 1729.0, 476.0, 1768.0, 305.0, 1768.0 ], "score": 0.998, "text": "{{premise}}" }, { "category_id": 15, "poly": [ 303.0, 606.0, 1209.0, 606.0, 1209.0, 642.0, 303.0, 642.0 ], "score": 0.98, "text": "Suppose it's true that {{premise}} Then， is \"{{hypothesis}}\"" }, { "category_id": 15, "poly": [ 306.0, 632.0, 1077.0, 632.0, 1077.0, 668.0, 306.0, 668.0 ], "score": 0.975, "text": "{{\"always\"}}，{{\"sometimes\"}}，or {{\"never\"}} true?" }, { "category_id": 15, "poly": [ 295.0, 1448.0, 574.0, 1448.0, 574.0, 1483.0, 295.0, 1483.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 307.0, 1369.0, 683.0, 1369.0, 683.0, 1401.0, 307.0, 1401.0 ], "score": 0.982, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 303.0, 1787.0, 1271.0, 1787.0, 1271.0, 1821.0, 303.0, 1821.0 ], "score": 0.982, "text": "Keeping in mind the above text， consider: {{hypothesis}} Is this" }, { "category_id": 15, "poly": [ 306.0, 1815.0, 1118.0, 1815.0, 1118.0, 1850.0, 306.0, 1850.0 ], "score": 0.982, "text": "{{\"always\"}}，{{\"sometimes\"}}，or {{\"never\"}} correct?" }, { "category_id": 15, "poly": [ 305.0, 805.0, 713.0, 805.0, 713.0, 842.0, 305.0, 842.0 ], "score": 0.971, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 304.0, 948.0, 762.0, 948.0, 762.0, 979.0, 304.0, 979.0 ], "score": 0.945, "text": "Always Ill Sometimes Ill Never" }, { "category_id": 15, "poly": [ 301.0, 382.0, 883.0, 382.0, 883.0, 417.0, 301.0, 417.0 ], "score": 0.978, "text": "Guaranteed Ill Possible Ill Impossible" }, { "category_id": 15, "poly": [ 306.0, 1986.0, 714.0, 1986.0, 714.0, 2022.0, 306.0, 2022.0 ], "score": 0.98, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 306.0, 241.0, 714.0, 241.0, 714.0, 277.0, 306.0, 277.0 ], "score": 0.981, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 301.0, 1505.0, 614.0, 1505.0, 614.0, 1544.0, 301.0, 1544.0 ], "score": 0.967, "text": "Yes IIl Maybe IIl No" }, { "category_id": 15, "poly": [ 294.0, 883.0, 575.0, 883.0, 575.0, 923.0, 294.0, 923.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1642.0, 713.0, 1642.0, 713.0, 1676.0, 295.0, 1676.0 ], "score": 0.989, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 1675.0, 463.0, 1675.0, 463.0, 1707.0, 295.0, 1707.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1306.0, 474.0, 1306.0, 474.0, 1344.0, 293.0, 1344.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 305.0, 1729.0, 476.0, 1729.0, 476.0, 1768.0, 305.0, 1768.0 ], "score": 0.998, "text": "{{premise}}" }, { "category_id": 15, "poly": [ 294.0, 516.0, 712.0, 516.0, 712.0, 550.0, 294.0, 550.0 ], "score": 0.98, "text": "Prompt from Webson and Pavlick (2021)" }, { "category_id": 15, "poly": [ 295.0, 550.0, 462.0, 550.0, 462.0, 582.0, 295.0, 582.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 1138.0, 475.0, 1138.0, 475.0, 1175.0, 304.0, 1175.0 ], "score": 0.999, "text": "{{premise}}" }, { "category_id": 15, "poly": [ 302.0, 1194.0, 1315.0, 1194.0, 1315.0, 1231.0, 302.0, 1231.0 ], "score": 0.979, "text": "Question: Does this imply that \"{{hypothesis}}\"? Yes， no, or maybe?" }, { "category_id": 15, "poly": [ 294.0, 745.0, 475.0, 745.0, 475.0, 783.0, 294.0, 783.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 319.0, 575.0, 319.0, 575.0, 359.0, 294.0, 359.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1080.0, 464.0, 1080.0, 464.0, 1116.0, 295.0, 1116.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.993, "text": "Published as a conference paper at ICLR 2022" } ], "page_info": { "page_no": 54, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 298, 1839, 1288, 1839, 1288, 2013, 298, 2013 ], "score": 0.972, "html": "
KeyValue
idx0
label1
sentence1Amrozi accused his brother ，whom he called " the
sentence2Referring to him as only " the witness " ，Amrozi：：
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Yes IIl Maybe III No
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"57" }, { "category_id": 15, "poly": [ 300.0, 1154.0, 463.0, 1154.0, 463.0, 1189.0, 300.0, 1189.0 ], "score": 0.852, "text": "no Ill yes" }, { "category_id": 15, "poly": [ 294.0, 1529.0, 474.0, 1529.0, 474.0, 1568.0, 294.0, 1568.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1095.0, 575.0, 1095.0, 575.0, 1135.0, 294.0, 1135.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1659.0, 575.0, 1659.0, 575.0, 1699.0, 295.0, 1699.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 301.0, 1338.0, 566.0, 1338.0, 566.0, 1373.0, 301.0, 1373.0 ], "score": 0.974, "text": "Does the sentence" }, { "category_id": 15, "poly": [ 306.0, 1368.0, 506.0, 1368.0, 506.0, 1399.0, 306.0, 1399.0 ], "score": 0.992, "text": "{{sentencel}}" }, { "category_id": 15, "poly": [ 300.0, 1394.0, 1197.0, 1394.0, 1197.0, 1429.0, 300.0, 1429.0 ], "score": 0.972, "text": "paraphrase (that is， mean the 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Template:" }, { "category_id": 15, "poly": [ 300.0, 1481.0, 463.0, 1481.0, 463.0, 1517.0, 300.0, 1517.0 ], "score": 0.944, "text": "no Ill yes" }, { "category_id": 15, "poly": [ 295.0, 1477.0, 470.0, 1477.0, 470.0, 1522.0, 295.0, 1522.0 ], "score": 0.83, "text": " no Ill yes" }, { "category_id": 15, "poly": [ 302.0, 1069.0, 701.0, 1069.0, 701.0, 1099.0, 302.0, 1099.0 ], "score": 0.998, "text": "Can I replace the sentence" }, { "category_id": 15, "poly": [ 305.0, 1096.0, 505.0, 1096.0, 505.0, 1126.0, 305.0, 1126.0 ], "score": 0.987, "text": "{{sentencel}}" }, { "category_id": 15, "poly": [ 301.0, 1120.0, 566.0, 1120.0, 566.0, 1156.0, 301.0, 1156.0 ], "score": 1.0, "text": "with the sentence" }, { "category_id": 15, "poly": [ 304.0, 1151.0, 505.0, 1151.0, 505.0, 1184.0, 304.0, 1184.0 ], "score": 1.0, "text": "{{sentence2}}" }, { "category_id": 15, "poly": [ 301.0, 1176.0, 792.0, 1176.0, 792.0, 1210.0, 301.0, 1210.0 ], "score": 0.998, "text": "and have it mean the same thing?" }, { "category_id": 15, "poly": [ 300.0, 1671.0, 1075.0, 1671.0, 1075.0, 1705.0, 300.0, 1705.0 ], "score": 0.984, "text": " Do the following two sentences mean the same thing?" }, { "category_id": 15, "poly": [ 306.0, 1702.0, 503.0, 1702.0, 503.0, 1731.0, 306.0, 1731.0 ], "score": 0.985, "text": "{{sentencel}}" }, { "category_id": 15, "poly": [ 304.0, 1726.0, 504.0, 1726.0, 504.0, 1760.0, 304.0, 1760.0 ], "score": 0.999, "text": "{{sentence2}}" }, { "category_id": 15, "poly": [ 294.0, 587.0, 933.0, 587.0, 933.0, 623.0, 294.0, 623.0 ], "score": 0.997, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 621.0, 462.0, 621.0, 462.0, 653.0, 296.0, 653.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 675.0, 479.0, 675.0, 479.0, 710.0, 304.0, 710.0 ], "score": 0.973, "text": "{% if label" }, { "category_id": 15, "poly": [ 548.0, 675.0, 596.0, 675.0, 596.0, 710.0, 548.0, 710.0 ], "score": 0.999, "text": "}" }, { "category_id": 15, "poly": [ 302.0, 701.0, 1383.0, 701.0, 1383.0, 737.0, 302.0, 737.0 ], "score": 0.99, "text": "Generate a sentence that means the same thing as this one: {{sentencel}}" }, { "category_id": 15, "poly": [ 306.0, 1347.0, 713.0, 1347.0, 713.0, 1379.0, 306.0, 1379.0 ], "score": 0.961, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 307.0, 321.0, 713.0, 321.0, 713.0, 353.0, 307.0, 353.0 ], "score": 0.958, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 306.0, 1897.0, 713.0, 1897.0, 713.0, 1929.0, 306.0, 1929.0 ], "score": 0.951, "text": "{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 295.0, 1971.0, 574.0, 1971.0, 574.0, 2009.0, 295.0, 2009.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 872.0, 505.0, 872.0, 505.0, 904.0, 304.0, 904.0 ], "score": 0.998, "text": "{{sentence2}}" }, { "category_id": 15, "poly": [ 304.0, 899.0, 477.0, 899.0, 477.0, 934.0, 304.0, 934.0 ], "score": 0.961, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 302.0, 457.0, 743.0, 457.0, 743.0, 489.0, 302.0, 489.0 ], "score": 0.995, "text": "not equivalent Ill equivalent" }, { "category_id": 15, "poly": [ 295.0, 1423.0, 574.0, 1423.0, 574.0, 1458.0, 295.0, 1458.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 394.0, 575.0, 394.0, 575.0, 434.0, 294.0, 434.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1008.0, 463.0, 1008.0, 463.0, 1044.0, 294.0, 1044.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1836.0, 474.0, 1836.0, 474.0, 1874.0, 293.0, 1874.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1286.0, 474.0, 1286.0, 474.0, 1324.0, 293.0, 1324.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 260.0, 474.0, 260.0, 474.0, 298.0, 293.0, 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KeyValue
idx 0 label
0
How is the life of a math student? Could you descr...
question1 question2Which level of prepration is enough for the exam
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There are two posts， one that" }, { "category_id": 15, "poly": [ 296.0, 1016.0, 1379.0, 1016.0, 1379.0, 1062.0, 296.0, 1062.0 ], "score": 0.94, "text": "asks \"{{questionl}}\" and another that asks \"{{question2]}\". I can merge" }, { "category_id": 15, "poly": [ 298.0, 1047.0, 1303.0, 1047.0, 1303.0, 1087.0, 298.0, 1087.0 ], "score": 0.996, "text": "questions if they are asking the same thing. Can I merge these two" }, { "category_id": 15, "poly": [ 300.0, 1079.0, 463.0, 1079.0, 463.0, 1111.0, 300.0, 1111.0 ], "score": 1.0, "text": "questions?" }, { "category_id": 15, "poly": [ 302.0, 1588.0, 506.0, 1588.0, 506.0, 1620.0, 302.0, 1620.0 ], "score": 0.974, "text": "{{questionl}}" }, { "category_id": 15, "poly": [ 303.0, 1617.0, 505.0, 1617.0, 505.0, 1650.0, 303.0, 1650.0 ], "score": 0.997, "text": "{{question2}}" }, { "category_id": 15, "poly": [ 299.0, 1641.0, 1198.0, 1641.0, 1198.0, 1680.0, 299.0, 1680.0 ], "score": 0.978, "text": " Pick one: These questions are \"{{\"duplicates\"}}\" or \"{{\"not" }, { "category_id": 15, "poly": [ 300.0, 1672.0, 528.0, 1672.0, 528.0, 1704.0, 300.0, 1704.0 ], "score": 0.998, "text": "duplicates\"}}\"." }, { "category_id": 15, "poly": [ 296.0, 466.0, 867.0, 466.0, 867.0, 503.0, 296.0, 503.0 ], "score": 0.976, "text": "Dataset from Iyer et al. (2017). 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KeyValue
id1
label0
sentence1 In Paris ，in October 1560 ，he secretly met the E...
sentence2In October 1560 ，he secretly met with the English...
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"Target Template:" }, { "category_id": 15, "poly": [ 296.0, 368.0, 469.0, 368.0, 469.0, 412.0, 296.0, 412.0 ], "score": 0.902, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 297.0, 906.0, 466.0, 906.0, 466.0, 946.0, 297.0, 946.0 ], "score": 0.93, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 294.0, 850.0, 575.0, 850.0, 575.0, 890.0, 294.0, 890.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 300.0, 1505.0, 682.0, 1505.0, 682.0, 1539.0, 300.0, 1539.0 ], "score": 0.983, "text": "Sentence 1: {{sentencel}}" }, { "category_id": 15, "poly": [ 302.0, 1532.0, 684.0, 1532.0, 684.0, 1566.0, 302.0, 1566.0 ], "score": 0.99, "text": "Sentence 2: {{sentence2}}" }, { "category_id": 15, "poly": [ 301.0, 1557.0, 1032.0, 1557.0, 1032.0, 1595.0, 301.0, 1595.0 ], "score": 0.999, "text": "Question: Does Sentence 1 paraphrase Sentence 2?" }, { "category_id": 15, "poly": [ 302.0, 559.0, 682.0, 559.0, 682.0, 593.0, 302.0, 593.0 ], "score": 0.956, 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"category_id": 15, "poly": [ 294.0, 1982.0, 475.0, 1982.0, 475.0, 2021.0, 294.0, 2021.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 871.0, 2085.0, 871.0, 2122.0, 830.0, 2122.0 ], "score": 1.0, "text": "64" }, { "category_id": 15, "poly": [ 295.0, 1784.0, 632.0, 1784.0, 632.0, 1823.0, 295.0, 1823.0 ], "score": 0.998, "text": "Prompt from Brown et al. 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KeyValue
answerKey
choices{'label':['A'，'B'，'C'，'D']，'text':['dry palm...
id questionMercury_SC_415702 George wants to warm his hands quickly by rubbing ...
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Used in evaluation." }, { "category_id": 15, "poly": [ 307.0, 244.0, 683.0, 244.0, 683.0, 275.0, 307.0, 275.0 ], "score": 0.99, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 305.0, 1298.0, 490.0, 1298.0, 490.0, 1339.0, 305.0, 1339.0 ], "score": 0.995, "text": "{{question}}" }, { "category_id": 15, "poly": [ 302.0, 1849.0, 891.0, 1849.0, 891.0, 1882.0, 302.0, 1882.0 ], "score": 0.985, "text": "Here's a problem to solve: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 1904.0, 1197.0, 1904.0, 1197.0, 1935.0, 302.0, 1935.0 ], "score": 0.98, "text": "Among the 4 following options， which is the correct answer?" }, { "category_id": 15, "poly": [ 305.0, 1930.0, 1102.0, 1930.0, 1102.0, 1965.0, 305.0, 1965.0 ], "score": 0.974, "text": "{% for letter，t in zip(answer_choices， choices.text)" }, { "category_id": 15, "poly": [ 1112.0, 1932.0, 1148.0, 1932.0, 1148.0, 1964.0, 1112.0, 1964.0 ], "score": 1.0, "text": "}" }, { "category_id": 15, "poly": [ 311.0, 1962.0, 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"Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1789.0, 463.0, 1789.0, 463.0, 1826.0, 295.0, 1826.0 ], "score": 0.971, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 845.0, 474.0, 845.0, 474.0, 883.0, 293.0, 883.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 260.0, 475.0, 260.0, 475.0, 298.0, 294.0, 298.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 592.0, 463.0, 592.0, 463.0, 628.0, 294.0, 628.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 318.0, 505.0, 318.0, 505.0, 355.0, 304.0, 355.0 ], "score": 0.987, "text": "{{answerKey}}" }, { "category_id": 15, "poly": [ 294.0, 981.0, 575.0, 981.0, 575.0, 1021.0, 294.0, 1021.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1594.0, 575.0, 1594.0, 575.0, 1634.0, 294.0, 1634.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 397.0, 575.0, 397.0, 575.0, 437.0, 294.0, 437.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 298.0, 458.0, 597.0, 458.0, 597.0, 492.0, 298.0, 492.0 ], "score": 0.964, "text": "AIIIBIIICIIID" }, { "category_id": 15, "poly": [ 300.0, 1231.0, 1313.0, 1231.0, 1313.0, 1272.0, 300.0, 1272.0 ], "score": 0.993, "text": " I am hesitating between 4 options to answer the following question," }, { "category_id": 15, "poly": [ 301.0, 1263.0, 746.0, 1263.0, 746.0, 1297.0, 301.0, 1297.0 ], "score": 0.985, "text": "which option should I choose?" }, { "category_id": 15, "poly": [ 302.0, 1291.0, 640.0, 1291.0, 640.0, 1327.0, 302.0, 1327.0 ], "score": 0.986, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 301.0, 1319.0, 521.0, 1319.0, 521.0, 1353.0, 301.0, 1353.0 ], "score": 1.0, "text": "Possibilities:" }, { "category_id": 15, "poly": [ 334.0, 1342.0, 834.0, 1342.0, 834.0, 1385.0, 334.0, 1385.0 ], "score": 0.957, "text": "{{answer_choices l join(\"\\n- \")}}" }, { "category_id": 15, "poly": [ 305.0, 1515.0, 1102.0, 1515.0, 1102.0, 1552.0, 305.0, 1552.0 ], "score": 0.988, "text": "{{answer_choices[choices[\"label\"].index(answerKey)]}}" }, { "category_id": 15, "poly": [ 306.0, 1044.0, 757.0, 1044.0, 757.0, 1078.0, 306.0, 1078.0 ], "score": 0.943, "text": "{{choices.text | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 306.0, 904.0, 1102.0, 904.0, 1102.0, 940.0, 306.0, 940.0 ], "score": 0.991, "text": "{{answer_choices[choices[\"label\"].index(answerKey)]}}" }, { "category_id": 15, "poly": [ 306.0, 1655.0, 758.0, 1655.0, 758.0, 1690.0, 306.0, 1690.0 ], "score": 0.947, "text": "{{choices.text | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 294.0, 397.0, 575.0, 397.0, 575.0, 437.0, 294.0, 437.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 981.0, 575.0, 981.0, 575.0, 1021.0, 294.0, 1021.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 1988.0, 894.0, 1988.0, 894.0, 2019.0, 304.0, 2019.0 ], "score": 0.997, "text": "Could you tell me which one is correct?" }, { "category_id": 15, "poly": [ 305.0, 320.0, 506.0, 320.0, 506.0, 355.0, 305.0, 355.0 ], "score": 0.995, "text": "{{answerKey}}" }, { "category_id": 15, "poly": [ 294.0, 1594.0, 575.0, 1594.0, 575.0, 1634.0, 294.0, 1634.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 306.0, 651.0, 489.0, 651.0, 489.0, 686.0, 306.0, 686.0 ], "score": 0.999, "text": "{{question}}" }, { "category_id": 15, "poly": [ 303.0, 708.0, 431.0, 708.0, 431.0, 739.0, 303.0, 739.0 ], "score": 1.0, "text": "Options:" }, { "category_id": 15, "poly": [ 294.0, 592.0, 463.0, 592.0, 463.0, 628.0, 294.0, 628.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 302.0, 1904.0, 1046.0, 1904.0, 1046.0, 1937.0, 302.0, 1937.0 ], "score": 0.993, "text": "Only one answer is correct among these 4 choices:" }, { "category_id": 15, "poly": [ 337.0, 1929.0, 832.0, 1929.0, 832.0, 1966.0, 337.0, 1966.0 ], "score": 0.971, "text": "{{answer_choices I join(\"\\n- \") }}" }, { "category_id": 15, "poly": [ 312.0, 735.0, 835.0, 735.0, 835.0, 769.0, 312.0, 769.0 ], "score": 0.958, "text": "- {{answer_choices | join(\"\\n- \") }}" }, { "category_id": 15, "poly": [ 298.0, 1849.0, 1238.0, 1849.0, 1238.0, 1883.0, 298.0, 1883.0 ], "score": 0.99, "text": " I gave my students this multiple choice question: {{question}}" }, { "category_id": 15, "poly": [ 294.0, 260.0, 475.0, 260.0, 475.0, 298.0, 294.0, 298.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 845.0, 474.0, 845.0, 474.0, 883.0, 293.0, 883.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1789.0, 463.0, 1789.0, 463.0, 1826.0, 295.0, 1826.0 ], "score": 0.972, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1457.0, 474.0, 1457.0, 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Key Value
answerKey B
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id questionMercury_7220990 Which factor will most likely cause a person to de...
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1884.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 617.0, 575.0, 617.0, 575.0, 657.0, 294.0, 657.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 306.0, 1195.0, 940.0, 1195.0, 940.0, 1227.0, 306.0, 1227.0 ], "score": 0.985, "text": "{% for i in range(choices[\"label\"]llength)" }, { "category_id": 15, "poly": [ 943.0, 1201.0, 979.0, 1201.0, 979.0, 1224.0, 943.0, 1224.0 ], "score": 0.938, "text": "%}" }, { "category_id": 15, "poly": [ 305.0, 1222.0, 436.0, 1222.0, 436.0, 1256.0, 305.0, 1256.0 ], "score": 0.894, "text": "{%ifi！" }, { "category_id": 15, "poly": [ 463.0, 1222.0, 966.0, 1222.0, 966.0, 1256.0, 463.0, 1256.0 ], "score": 0.988, "text": " choices[\"label\"].index(answerKey)" }, { "category_id": 15, "poly": [ 305.0, 1250.0, 669.0, 1250.0, 669.0, 1283.0, 305.0, 1283.0 ], "score": 0.95, "text": "- {{choices[\"text\"][i]}}" }, { "category_id": 15, "poly": [ 307.0, 1277.0, 477.0, 1277.0, 477.0, 1310.0, 307.0, 1310.0 ], "score": 0.988, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 1302.0, 492.0, 1302.0, 492.0, 1340.0, 304.0, 1340.0 ], "score": 0.956, "text": "{% endfor %}" }, { "category_id": 15, "poly": [ 977.75, 1228.5, 1006.75, 1228.5, 1006.75, 1252.5, 977.75, 1252.5 ], "score": 0.985, "text": "%}" }, { "category_id": 15, "poly": [ 294.0, 804.0, 933.0, 804.0, 933.0, 842.0, 294.0, 842.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 840.0, 462.0, 840.0, 462.0, 872.0, 296.0, 872.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 543.0, 1102.0, 543.0, 1102.0, 579.0, 306.0, 579.0 ], "score": 0.991, "text": "{{answer_choices[choices[\"label\"].index(answerKey)]}}" }, { "category_id": 15, "poly": [ 307.0, 676.0, 758.0, 676.0, 758.0, 710.0, 307.0, 710.0 ], "score": 0.952, "text": "{{choices.text I join(\"lll\")}}" }, { "category_id": 15, "poly": [ 294.0, 1844.0, 575.0, 1844.0, 575.0, 1884.0, 294.0, 1884.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 300.0, 888.0, 1314.0, 888.0, 1314.0, 928.0, 300.0, 928.0 ], "score": 0.998, "text": " Pick and copy all the incorrect options for the following question:" }, { "category_id": 15, "poly": [ 306.0, 947.0, 489.0, 947.0, 489.0, 983.0, 306.0, 983.0 ], "score": 0.998, "text": "{{question}}" }, { "category_id": 15, "poly": [ 302.0, 1469.0, 892.0, 1469.0, 892.0, 1502.0, 302.0, 1502.0 ], "score": 0.971, "text": " Here's a problem to solve: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 297.0, 1043.0, 297.0, 1043.0, 330.0, 302.0, 330.0 ], "score": 0.997, "text": "Only one answer is correct among these 4 choices:" }, { "category_id": 15, "poly": [ 337.0, 325.0, 768.0, 325.0, 768.0, 357.0, 337.0, 357.0 ], "score": 0.963, "text": "{{answer_choices | join(\"\\n-" }, { "category_id": 15, "poly": [ 303.0, 381.0, 896.0, 381.0, 896.0, 413.0, 303.0, 413.0 ], "score": 0.997, "text": "Could you tell me which one is correct?" }, { "category_id": 15, "poly": [ 303.0, 1005.0, 431.0, 1005.0, 431.0, 1036.0, 303.0, 1036.0 ], "score": 1.0, "text": "Options:" }, { "category_id": 15, "poly": [ 294.0, 617.0, 575.0, 617.0, 575.0, 657.0, 294.0, 657.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 293.0, 1714.0, 474.0, 1714.0, 474.0, 1752.0, 293.0, 1752.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 305.0, 1769.0, 506.0, 1769.0, 506.0, 1807.0, 305.0, 1807.0 ], "score": 0.992, "text": "{{answerKey}}" }, { "category_id": 15, "poly": [ 298.0, 242.0, 1237.0, 242.0, 1237.0, 276.0, 298.0, 276.0 ], "score": 0.991, "text": " I gave my students this multiple choice question: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 1523.0, 1192.0, 1523.0, 1192.0, 1557.0, 302.0, 1557.0 ], "score": 0.978, "text": "Among the 4 following options， which is the correct answer?" }, { "category_id": 15, "poly": [ 305.0, 1550.0, 1105.0, 1550.0, 1105.0, 1586.0, 305.0, 1586.0 ], "score": 0.969, "text": "{% for letter，t in zip(answer_choices, choices.text)" }, { "category_id": 15, "poly": [ 1110.0, 1554.0, 1147.0, 1554.0, 1147.0, 1582.0, 1110.0, 1582.0 ], "score": 0.985, "text": "%}" }, { "category_id": 15, "poly": [ 332.0, 1580.0, 594.0, 1580.0, 594.0, 1612.0, 332.0, 1612.0 ], "score": 0.993, "text": "{{letter}}:{{t}}" }, { "category_id": 15, "poly": [ 319.0, 1608.0, 505.0, 1608.0, 505.0, 1640.0, 319.0, 1640.0 ], "score": 0.978, "text": "{% endfor %}" }, { "category_id": 15, "poly": [ 293.0, 1138.0, 474.0, 1138.0, 474.0, 1176.0, 293.0, 1176.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 310.0, 1029.0, 849.0, 1029.0, 849.0, 1066.0, 310.0, 1066.0 ], "score": 0.971, "text": "- {{choices[\"text\"] | join(\"\\n- \")}}" }, { "category_id": 15, "poly": [ 294.0, 487.0, 475.0, 487.0, 475.0, 526.0, 294.0, 526.0 ], "score": 0.999, 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Key
id5a7a06935542990198eaf050
inputWhich magazine was started first Arthur's Magazine...
meta output'mention': 'right_context... [{'answer': "Arthur's Magazine", 'meta': {'score':.
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684, 1534, 684, 1554, 665, 1554 ], "score": 0.63, "latex": "=" }, { "category_id": 13, "poly": [ 665, 1103, 683, 1103, 683, 1123, 665, 1123 ], "score": 0.63, "latex": "=" }, { "category_id": 13, "poly": [ 665, 1966, 683, 1966, 683, 1986, 665, 1986 ], "score": 0.61, "latex": "=" }, { "category_id": 15, "poly": [ 292.0, 669.0, 408.0, 669.0, 408.0, 711.0, 292.0, 711.0 ], "score": 1.0, "text": "Prompts" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.993, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 296.0, 227.0, 668.0, 227.0, 668.0, 267.0, 296.0, 267.0 ], "score": 0.98, "text": "1.5.3KILT-TASKS HOTPOTQA" }, { "category_id": 15, "poly": [ 293.0, 1898.0, 474.0, 1898.0, 474.0, 1936.0, 293.0, 1936.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 831.0, 2085.0, 870.0, 2085.0, 870.0, 2123.0, 831.0, 2123.0 ], "score": 1.0, "text": "70" }, { "category_id": 15, "poly": [ 294.0, 1466.0, 473.0, 1466.0, 473.0, 1504.0, 294.0, 1504.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1035.0, 474.0, 1035.0, 474.0, 1073.0, 293.0, 1073.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 348.0, 472.0, 348.0, 472.0, 387.0, 294.0, 387.0 ], "score": 0.999, "text": "Data Example" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 871.0, 2085.0, 871.0, 2125.0, 830.0, 2125.0 ], "score": 1.0, "text": "70" }, { "category_id": 15, "poly": [ 294.0, 749.0, 933.0, 749.0, 933.0, 785.0, 294.0, 785.0 ], "score": 0.997, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 783.0, 462.0, 783.0, 462.0, 815.0, 296.0, 815.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1234.0, 933.0, 1234.0, 933.0, 1272.0, 294.0, 1272.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 1270.0, 462.0, 1270.0, 462.0, 1302.0, 295.0, 1302.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1665.0, 933.0, 1665.0, 933.0, 1703.0, 294.0, 1703.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1701.0, 462.0, 1701.0, 462.0, 1733.0, 296.0, 1733.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 1096.0, 664.0, 1096.0, 664.0, 1130.0, 306.0, 1130.0 ], "score": 0.968, "text": "{{output | map(attribute" }, { "category_id": 15, "poly": [ 684.0, 1096.0, 1086.0, 1096.0, 1086.0, 1130.0, 684.0, 1130.0 ], "score": 0.934, "text": "\"answer\") | list | choice}}" }, { "category_id": 15, "poly": [ 306.0, 1124.0, 477.0, 1124.0, 477.0, 1159.0, 306.0, 1159.0 ], "score": 0.932, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 294.0, 279.0, 669.0, 279.0, 669.0, 322.0, 294.0, 322.0 ], "score": 0.996, "text": "Dataset from ?. Used in training." }, { "category_id": 15, "poly": [ 305.0, 1527.0, 664.0, 1527.0, 664.0, 1561.0, 305.0, 1561.0 ], "score": 0.94, "text": "{{output | map(attribute" }, { "category_id": 15, "poly": [ 685.0, 1527.0, 1087.0, 1527.0, 1087.0, 1561.0, 685.0, 1561.0 ], "score": 0.924, "text": "\"answer\") | list | choice}}" }, { "category_id": 15, "poly": [ 306.0, 1557.0, 475.0, 1557.0, 475.0, 1589.0, 306.0, 1589.0 ], "score": 0.962, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 1326.0, 534.0, 1326.0, 534.0, 1359.0, 304.0, 1359.0 ], "score": 0.994, "text": "{% if output %}" }, { "category_id": 15, "poly": [ 301.0, 1351.0, 909.0, 1351.0, 909.0, 1391.0, 301.0, 1391.0 ], "score": 0.987, "text": "Combine facts and answer this: {{input}}" }, { "category_id": 15, "poly": [ 305.0, 1959.0, 664.0, 1959.0, 664.0, 1993.0, 305.0, 1993.0 ], "score": 0.94, "text": "{{output | map(attribute" }, { "category_id": 15, "poly": [ 684.0, 1959.0, 1087.0, 1959.0, 1087.0, 1993.0, 684.0, 1993.0 ], "score": 0.927, "text": "\"answer\") | list | choice}}" }, { "category_id": 15, "poly": [ 305.0, 1986.0, 476.0, 1986.0, 476.0, 2021.0, 305.0, 2021.0 ], "score": 0.976, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 1758.0, 535.0, 1758.0, 535.0, 1790.0, 304.0, 1790.0 ], "score": 0.973, "text": "{% if output %}" }, { "category_id": 15, "poly": [ 298.0, 1780.0, 1162.0, 1780.0, 1162.0, 1823.0, 298.0, 1823.0 ], "score": 0.987, "text": " Formulate an answer to this elaborate question: {{input}}" }, { "category_id": 15, "poly": [ 302.0, 838.0, 538.0, 838.0, 538.0, 874.0, 302.0, 874.0 ], "score": 0.961, "text": "{% if output %}" }, { "category_id": 15, "poly": [ 298.0, 866.0, 1318.0, 866.0, 1318.0, 905.0, 298.0, 905.0 ], "score": 0.996, "text": " Here's a complex question that requires someone to reason about the" }, { "category_id": 15, "poly": [ 301.0, 896.0, 690.0, 896.0, 690.0, 927.0, 301.0, 927.0 ], "score": 0.979, "text": "input， can you answer it?" }, { "category_id": 15, "poly": [ 302.0, 924.0, 448.0, 924.0, 448.0, 961.0, 302.0, 961.0 ], "score": 0.997, "text": "{{input}}" }, { "category_id": 15, "poly": [ 293.0, 1035.0, 474.0, 1035.0, 474.0, 1073.0, 293.0, 1073.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1466.0, 473.0, 1466.0, 473.0, 1504.0, 294.0, 1504.0 ], "score": 0.999, "text": "Target Template:" } ], "page_info": { "page_no": 69, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 297, 1396, 1346, 1396, 1346, 1624, 297, 1624 ], "score": 0.975, "html": "
KeyValue
question question_id question_source entity-pagesWho was President when the first Peanuts cartoon w... tc_0 http://www.triviacountry.com/
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(2O17). Used in evaluation." }, { "category_id": 15, "poly": [ 305.0, 1018.0, 664.0, 1018.0, 664.0, 1054.0, 305.0, 1054.0 ], "score": 0.973, "text": "{{output 丨 map(attribute" }, { "category_id": 15, "poly": [ 684.0, 1018.0, 1088.0, 1018.0, 1088.0, 1054.0, 684.0, 1054.0 ], "score": 0.916, "text": "\"answer\") | list 丨 choice}}" }, { "category_id": 15, "poly": [ 303.0, 1047.0, 476.0, 1047.0, 476.0, 1080.0, 303.0, 1080.0 ], "score": 0.966, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 598.0, 664.0, 598.0, 664.0, 635.0, 305.0, 635.0 ], "score": 0.979, "text": "{{output I map(attribute" }, { "category_id": 15, "poly": [ 684.0, 598.0, 1087.0, 598.0, 1087.0, 635.0, 684.0, 635.0 ], "score": 0.923, "text": "\"answer\"） | list 丨 choice}}" }, { "category_id": 15, "poly": [ 305.0, 627.0, 478.0, 627.0, 478.0, 661.0, 305.0, 661.0 ], "score": 0.951, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 824.0, 496.0, 824.0, 496.0, 858.0, 304.0, 858.0 ], "score": 0.995, "text": "{% if output" }, { "category_id": 15, "poly": [ 501.0, 830.0, 531.0, 830.0, 531.0, 854.0, 501.0, 854.0 ], "score": 0.964, "text": "%}" }, { "category_id": 15, "poly": [ 305.0, 852.0, 446.0, 852.0, 446.0, 886.0, 305.0, 886.0 ], "score": 0.999, "text": "{{input}}" }, { "category_id": 15, "poly": [ 307.0, 1845.0, 613.0, 1845.0, 613.0, 1876.0, 307.0, 1876.0 ], "score": 0.982, "text": "{% if answer.aliases" }, { "category_id": 15, "poly": [ 617.0, 1847.0, 653.0, 1847.0, 653.0, 1875.0, 617.0, 1875.0 ], "score": 0.763, "text": "%}" }, { "category_id": 15, "poly": [ 362.0, 1870.0, 1327.0, 1870.0, 1327.0, 1905.0, 362.0, 1905.0 ], "score": 0.988, "text": "Guess a question that has the answer \"{{answer.aliaseslchoice}}\"" }, { "category_id": 15, "poly": [ 303.0, 433.0, 622.0, 433.0, 622.0, 465.0, 303.0, 465.0 ], "score": 0.978, "text": "Question 1. {{input}}" }, { "category_id": 15, "poly": [ 305.0, 348.0, 531.0, 348.0, 531.0, 382.0, 305.0, 382.0 ], "score": 0.992, "text": "{% if output %}" }, { 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}, { "category_id": 15, "poly": [ 293.0, 1408.0, 474.0, 1408.0, 474.0, 1446.0, 293.0, 1446.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1626.0, 463.0, 1626.0, 463.0, 1663.0, 295.0, 1663.0 ], "score": 0.972, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 376.0, 463.0, 376.0, 463.0, 412.0, 294.0, 412.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 242.0, 489.0, 242.0, 489.0, 275.0, 306.0, 275.0 ], "score": 0.999, "text": "{{question}}" }, { "category_id": 15, "poly": [ 305.0, 267.0, 478.0, 267.0, 478.0, 303.0, 305.0, 303.0 ], "score": 0.934, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 870.0, 2085.0, 870.0, 2126.0, 830.0, 2126.0 ], "score": 1.0, "text": "72" }, { "category_id": 15, "poly": [ 306.0, 676.0, 654.0, 676.0, 654.0, 709.0, 306.0, 709.0 ], "score": 0.984, "text": "{% if answer.aliases %}" }, { "category_id": 15, "poly": [ 307.0, 706.0, 681.0, 706.0, 681.0, 736.0, 307.0, 736.0 ], "score": 0.962, "text": "{{answer.aliaseslchoice} }" }, { "category_id": 15, "poly": [ 305.0, 732.0, 475.0, 732.0, 475.0, 765.0, 305.0, 765.0 ], "score": 0.96, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 1463.0, 652.0, 1463.0, 652.0, 1495.0, 306.0, 1495.0 ], "score": 0.992, "text": "{% if answer.aliases %}" }, { "category_id": 15, "poly": [ 306.0, 1493.0, 683.0, 1493.0, 683.0, 1524.0, 306.0, 1524.0 ], "score": 0.981, "text": "{{answer.aliases|choice}}" }, { "category_id": 15, "poly": [ 305.0, 1520.0, 476.0, 1520.0, 476.0, 1553.0, 305.0, 1553.0 ], "score": 0.957, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 1083.0, 654.0, 1083.0, 654.0, 1116.0, 306.0, 1116.0 ], "score": 0.984, "text": "{% if answer.aliases %}" }, { "category_id": 15, "poly": [ 307.0, 1113.0, 683.0, 1113.0, 683.0, 1144.0, 307.0, 1144.0 ], "score": 0.979, "text": "{{answer.aliaseslchoice}}" }, { "category_id": 15, "poly": [ 304.0, 1140.0, 476.0, 1140.0, 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KeyValue
answers['Jazmyn Bieber'，'Jaxon Bieber']
questionwhat is the name of justin bieber brother?
urlhttp://www.freebase.com/view/en/justin_bieber
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answer document_title label 0African immigration to the United States refers to... African immigration to the United States
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15, "poly": [ 295.0, 380.0, 469.0, 380.0, 469.0, 422.0, 295.0, 422.0 ], "score": 0.927, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 295.0, 320.0, 575.0, 320.0, 575.0, 360.0, 295.0, 360.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 239.0, 683.0, 239.0, 683.0, 279.0, 304.0, 279.0 ], "score": 0.983, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 294.0, 517.0, 933.0, 517.0, 933.0, 555.0, 294.0, 555.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 553.0, 462.0, 553.0, 462.0, 585.0, 295.0, 585.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1410.0, 933.0, 1410.0, 933.0, 1446.0, 294.0, 1446.0 ], "score": 0.997, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1444.0, 462.0, 1444.0, 462.0, 1476.0, 296.0, 1476.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 950.0, 933.0, 950.0, 933.0, 986.0, 294.0, 986.0 ], "score": 0.997, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 985.0, 462.0, 985.0, 462.0, 1017.0, 296.0, 1017.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 307.0, 1273.0, 578.0, 1273.0, 578.0, 1304.0, 307.0, 1304.0 ], "score": 0.998, "text": "{{document_title}}" }, { "category_id": 15, "poly": [ 305.0, 1299.0, 476.0, 1299.0, 476.0, 1333.0, 305.0, 1333.0 ], "score": 0.911, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 303.0, 1933.0, 640.0, 1933.0, 640.0, 1966.0, 303.0, 1966.0 ], "score": 0.987, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 298.0, 1950.0, 1059.0, 1950.0, 1059.0, 2001.0, 298.0, 2001.0 ], "score": 0.97, "text": "I found the following answer on Google: ((answer])" }, { "category_id": 15, "poly": [ 298.0, 1982.0, 851.0, 1982.0, 851.0, 2024.0, 298.0, 2024.0 ], "score": 0.955, "text": " Is that a correct answer? Yes or no." }, { "category_id": 15, "poly": [ 305.0, 609.0, 479.0, 609.0, 479.0, 644.0, 305.0, 644.0 ], "score": 0.966, "text": "{% if label" }, { "category_id": 15, "poly": [ 547.0, 609.0, 595.0, 609.0, 595.0, 644.0, 547.0, 644.0 ], "score": 0.956, "text": "%}" }, { "category_id": 15, "poly": [ 301.0, 637.0, 1372.0, 637.0, 1372.0, 671.0, 301.0, 671.0 ], "score": 0.992, "text": "What is the question to: \"{{answer}}\"? The topic is {{document_title}}." }, { "category_id": 15, "poly": [ 305.0, 1040.0, 465.0, 1040.0, 465.0, 1077.0, 305.0, 1077.0 ], "score": 0.95, "text": "{%if labe]" }, { "category_id": 16, "poly": [ 589.0, 1040.0, 597.0, 1040.0, 597.0, 1077.0, 589.0, 1077.0 ], "score": 0.0, "text": "" }, { "category_id": 15, "poly": [ 303.0, 1070.0, 1031.0, 1070.0, 1031.0, 1104.0, 303.0, 1104.0 ], "score": 0.993, "text": "Determine the topic of the question-answer pair." }, { "category_id": 15, "poly": [ 300.0, 1092.0, 1153.0, 1092.0, 1153.0, 1135.0, 300.0, 1135.0 ], "score": 0.983, "text": "Question: \"{{question}}?\"; Answer: \"{{answer}}\"? Topic:" }, { "category_id": 15, "poly": [ 303.0, 812.0, 536.0, 812.0, 536.0, 843.0, 303.0, 843.0 ], "score": 0.989, "text": "\"{{question}}?\"" }, { "category_id": 15, "poly": [ 305.0, 841.0, 475.0, 841.0, 475.0, 873.0, 305.0, 873.0 ], "score": 0.984, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 1731.0, 509.0, 1731.0, 509.0, 1762.0, 305.0, 1762.0 ], "score": 0.99, "text": "{{question}}?" }, { "category_id": 15, "poly": [ 304.0, 1760.0, 475.0, 1760.0, 475.0, 1790.0, 304.0, 1790.0 ], "score": 0.977, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 307.0, 244.0, 683.0, 244.0, 683.0, 275.0, 307.0, 275.0 ], "score": 0.99, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 295.0, 320.0, 575.0, 320.0, 575.0, 360.0, 295.0, 360.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 1502.0, 477.0, 1502.0, 477.0, 1535.0, 304.0, 1535.0 ], "score": 0.975, "text": "{% if label" }, { "category_id": 16, "poly": [ 584.0, 1502.0, 596.0, 1502.0, 596.0, 1535.0, 584.0, 1535.0 ], "score": 0.0, "text": "" }, { "category_id": 15, "poly": [ 302.0, 1528.0, 1346.0, 1528.0, 1346.0, 1563.0, 302.0, 1563.0 ], "score": 0.998, "text": "Generate a question about the topic \"{{document_title}}\" whose answer" }, { "category_id": 15, "poly": [ 302.0, 1559.0, 619.0, 1559.0, 619.0, 1591.0, 302.0, 1591.0 ], "score": 0.986, "text": "would be: {{answer}}." }, { "category_id": 15, "poly": [ 293.0, 1210.0, 474.0, 1210.0, 474.0, 1248.0, 293.0, 1248.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1871.0, 463.0, 1871.0, 463.0, 1907.0, 294.0, 1907.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1669.0, 474.0, 1669.0, 474.0, 1707.0, 293.0, 1707.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 750.0, 474.0, 750.0, 474.0, 788.0, 293.0, 788.0 ], "score": 0.998, "text": "Target Template:" } ], "page_info": { 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721, 471, 721 ], "score": 0.28, "latex": "= = \\frac { 1 } { 0 } \\frac { 0 } { 0 } ]" }, { "category_id": 15, "poly": [ 295.0, 72.0, 817.0, 72.0, 817.0, 109.0, 295.0, 109.0 ], "score": 0.98, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 829.0, 2085.0, 872.0, 2085.0, 872.0, 2124.0, 829.0, 2124.0 ], "score": 1.0, "text": "76" }, { "category_id": 15, "poly": [ 295.0, 1713.0, 463.0, 1713.0, 463.0, 1749.0, 295.0, 1749.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 260.0, 474.0, 260.0, 474.0, 298.0, 293.0, 298.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1089.0, 463.0, 1089.0, 463.0, 1125.0, 295.0, 1125.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 299.0, 1577.0, 523.0, 1577.0, 523.0, 1612.0, 299.0, 1612.0 ], "score": 0.954, "text": "False IIl True" }, { "category_id": 15, "poly": [ 293.0, 1373.0, 474.0, 1373.0, 474.0, 1411.0, 293.0, 1411.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 887.0, 473.0, 887.0, 473.0, 925.0, 294.0, 925.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 831.0, 2085.0, 870.0, 2085.0, 870.0, 2123.0, 831.0, 2123.0 ], "score": 1.0, "text": "76" }, { "category_id": 15, "poly": [ 299.0, 462.0, 464.0, 462.0, 464.0, 500.0, 299.0, 500.0 ], "score": 0.904, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 306.0, 321.0, 683.0, 321.0, 683.0, 358.0, 306.0, 358.0 ], "score": 0.971, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 294.0, 401.0, 575.0, 401.0, 575.0, 441.0, 294.0, 441.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 1430.0, 684.0, 1430.0, 684.0, 1472.0, 304.0, 1472.0 ], "score": 0.969, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 298.0, 1576.0, 524.0, 1576.0, 524.0, 1613.0, 298.0, 1613.0 ], "score": 0.983, 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KeyValue
id821607441c173838196c4d1500c2ab21a044e6b0
titleYale_University
contextSlack (2003) compares three groups that conducted ...
questionwhat year were the research groups compared
answers{'text':['2003']， 'answer_start':[7]}
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Note that the answer is present within" }, { "category_id": 15, "poly": [ 298.0, 973.0, 446.0, 973.0, 446.0, 1012.0, 298.0, 1012.0 ], "score": 0.994, "text": "the text." }, { "category_id": 15, "poly": [ 304.0, 1817.0, 632.0, 1817.0, 632.0, 1850.0, 304.0, 1850.0 ], "score": 0.991, "text": "{% if metadata.split !" }, { "category_id": 15, "poly": [ 658.0, 1817.0, 807.0, 1817.0, 807.0, 1850.0, 658.0, 1850.0 ], "score": 0.986, "text": "\"test\" %}" }, { "category_id": 15, "poly": [ 301.0, 1842.0, 1198.0, 1842.0, 1198.0, 1878.0, 301.0, 1878.0 ], "score": 0.989, "text": "I know that the answer to the question \"{{question}}\" is in" }, { "category_id": 15, "poly": [ 299.0, 1869.0, 944.0, 1869.0, 944.0, 1907.0, 299.0, 1907.0 ], "score": 0.995, "text": "\"{{context}}\". 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KeyValue
id titledab017ed8a1c27c6afa2d8618abc3a477a4edffc Empiricism
contextA generation later，the Irish Anglican bishop，Geo...
questionwhat concept is mentioned last?
answers metadata{'text':['subjective idealism']，'answer_start':..
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{{question}}
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Could you please come up with a good question for" }, { "category_id": 15, "poly": [ 303.0, 1605.0, 702.0, 1605.0, 702.0, 1639.0, 303.0, 1639.0 ], "score": 0.995, "text": "the passage \"{{context}}\"?" }, { "category_id": 15, "poly": [ 293.0, 949.0, 881.0, 949.0, 881.0, 997.0, 293.0, 997.0 ], "score": 0.982, "text": "Datase from Bartolo et al. (2020). Used in training." }, { "category_id": 15, "poly": [ 302.0, 1930.0, 631.0, 1930.0, 631.0, 1965.0, 302.0, 1965.0 ], "score": 0.958, "text": "{% if metadata.split !" }, { "category_id": 15, "poly": [ 658.0, 1930.0, 803.0, 1930.0, 803.0, 1965.0, 658.0, 1965.0 ], "score": 0.944, "text": "\"test\" %}" }, { "category_id": 15, "poly": [ 299.0, 1957.0, 1198.0, 1957.0, 1198.0, 1996.0, 299.0, 1996.0 ], "score": 0.995, "text": "I know that the answer to the question \"{{question}}\" is in" }, { "category_id": 15, "poly": [ 299.0, 1982.0, 945.0, 1982.0, 945.0, 2024.0, 299.0, 2024.0 ], "score": 0.964, "text": "\"{{context}}\". 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KeyValue
id12cf36866b656dc4f254081fe6796ealbe2f6d43
titleNapoleon
contextWhen he became First Consul and later Emperor，Nap...
questionWhat jewelry like accessories did he wear?
answers{'text':["Légion d'honneur star，medal and ribbon...
metadata{'split':'train'，'model_in_the_loop':'RoBERTa-L...
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KeyValue
answers['They arrived by train.']
no_answer plotFalse
plot_id200 years in the future，Mars has been colonized b... /m/03vyhn
questionHow did the police arrive at the Mars mining camp?
question_id titleb440de7d-9c3f-841c-eaec-al4bdff950d1 Ghosts of Mars
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"Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1653.0, 473.0, 1653.0, 473.0, 1692.0, 294.0, 1692.0 ], "score": 0.999, "text": "Data Example" }, { "category_id": 15, "poly": [ 293.0, 1270.0, 474.0, 1270.0, 474.0, 1308.0, 293.0, 1308.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 386.0, 463.0, 386.0, 463.0, 422.0, 294.0, 422.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 270.0, 474.0, 270.0, 474.0, 301.0, 305.0, 301.0 ], "score": 0.973, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 302.0, 449.0, 631.0, 449.0, 631.0, 487.0, 302.0, 487.0 ], "score": 0.946, "text": "{% if metadata.split !" }, { "category_id": 15, "poly": [ 658.0, 449.0, 806.0, 449.0, 806.0, 487.0, 658.0, 487.0 ], "score": 0.957, "text": "\"test\" %}" }, { "category_id": 15, "poly": [ 301.0, 479.0, 1236.0, 479.0, 1236.0, 514.0, 301.0, 514.0 ], "score": 1.0, "text": "Extract the answer to the question from the following context." }, { "category_id": 15, "poly": [ 303.0, 509.0, 639.0, 509.0, 639.0, 540.0, 303.0, 540.0 ], "score": 0.978, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 535.0, 609.0, 535.0, 609.0, 569.0, 302.0, 569.0 ], "score": 0.973, "text": "Context: {{context}}" }, { "category_id": 15, "poly": [ 304.0, 1154.0, 640.0, 1154.0, 640.0, 1186.0, 304.0, 1186.0 ], "score": 0.99, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 307.0, 1338.0, 683.0, 1338.0, 683.0, 1369.0, 307.0, 1369.0 ], "score": 0.972, "text": "{{answers.text | choice}}" }, { "category_id": 15, "poly": [ 304.0, 1365.0, 476.0, 1365.0, 476.0, 1399.0, 304.0, 1399.0 ], "score": 0.976, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 307.0, 721.0, 683.0, 721.0, 683.0, 752.0, 307.0, 752.0 ], "score": 0.971, "text": "{{answers.text | choice}}" }, { "category_id": 15, "poly": [ 305.0, 749.0, 475.0, 749.0, 475.0, 780.0, 305.0, 780.0 ], "score": 0.977, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 302.0, 1069.0, 1347.0, 1069.0, 1347.0, 1103.0, 302.0, 1103.0 ], "score": 0.998, "text": "answer the following question. Note that the answer is present within" }, { "category_id": 15, "poly": [ 300.0, 1093.0, 447.0, 1093.0, 447.0, 1132.0, 300.0, 1132.0 ], "score": 0.995, "text": "the text." }, { "category_id": 15, "poly": [ 293.0, 1574.0, 852.0, 1574.0, 852.0, 1622.0, 293.0, 1622.0 ], "score": 0.981, "text": "Datase from Saha et al. (2018). Used in training." }, { "category_id": 15, "poly": [ 305.0, 929.0, 631.0, 929.0, 631.0, 965.0, 305.0, 965.0 ], "score": 0.972, "text": "{% if metadata.split !" }, { "category_id": 15, "poly": [ 658.0, 929.0, 805.0, 929.0, 805.0, 965.0, 658.0, 965.0 ], "score": 0.972, "text": "\"test\" %}" }, { "category_id": 15, "poly": [ 304.0, 958.0, 716.0, 958.0, 716.0, 994.0, 304.0, 994.0 ], "score": 1.0, "text": "Given the following passage" }, { "category_id": 15, "poly": [ 293.0, 1270.0, 474.0, 1270.0, 474.0, 1308.0, 293.0, 1308.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 386.0, 463.0, 386.0, 463.0, 422.0, 294.0, 422.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 302.0, 1012.0, 518.0, 1012.0, 518.0, 1050.0, 302.0, 1050.0 ], "score": 0.988, "text": "\"{{context}}\"," }, { "category_id": 15, "poly": [ 293.0, 653.0, 474.0, 653.0, 474.0, 691.0, 293.0, 691.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 305.0, 270.0, 474.0, 270.0, 474.0, 301.0, 305.0, 301.0 ], "score": 0.973, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 308.0, 243.0, 682.0, 243.0, 682.0, 274.0, 308.0, 274.0 ], "score": 0.961, "text": "{{answers.text | choice}}" }, { "category_id": 15, "poly": [ 294.0, 865.0, 463.0, 865.0, 463.0, 901.0, 294.0, 901.0 ], "score": 0.999, "text": "Input Template:" } ], "page_info": { "page_no": 81, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 304, 395, 1054, 395, 1054, 538, 304, 538 ], "score": 0.958 }, { "category_id": 1, "poly": [ 303, 972, 680, 972, 680, 1057, 303, 1057 ], "score": 0.955 }, { "category_id": 1, "poly": [ 304, 1464, 1394, 1464, 1394, 1608, 304, 1608 ], "score": 0.954 }, { "category_id": 1, "poly": [ 305, 1737, 622, 1737, 622, 1878, 305, 1878 ], "score": 0.951 }, { "category_id": 1, "poly": [ 304, 1189, 576, 1189, 576, 1330, 304, 1330 ], "score": 0.947 }, { "category_id": 1, "poly": [ 297, 309, 931, 309, 931, 370, 297, 370 ], "score": 0.94 }, { "category_id": 1, "poly": [ 296, 1955, 930, 1955, 930, 2016, 296, 2016 ], "score": 0.937 }, { "category_id": 1, "poly": [ 301, 860, 1366, 860, 1366, 944, 301, 944 ], "score": 0.929 }, { "category_id": 0, "poly": [ 297, 230, 403, 230, 403, 262, 297, 262 ], "score": 0.896 }, { "category_id": 2, "poly": [ 299, 75, 815, 75, 815, 105, 299, 105 ], "score": 0.882 }, { "category_id": 1, "poly": [ 308, 671, 484, 671, 484, 727, 308, 727 ], "score": 0.865 }, { "category_id": 2, "poly": [ 834, 2089, 864, 2089, 864, 2113, 834, 2113 ], "score": 0.854 }, { "category_id": 0, "poly": [ 299, 1682, 468, 1682, 468, 1712, 299, 1712 ], "score": 0.816 }, { "category_id": 0, "poly": [ 299, 1134, 468, 1134, 468, 1164, 299, 1164 ], "score": 0.713 }, { "category_id": 0, "poly": [ 298, 804, 458, 804, 458, 834, 298, 834 ], "score": 0.707 }, { "category_id": 0, "poly": [ 298, 1408, 458, 1408, 458, 1438, 298, 1438 ], "score": 0.706 }, { "category_id": 0, "poly": [ 298, 614, 468, 614, 468, 644, 298, 644 ], "score": 0.508 }, { "category_id": 1, "poly": [ 298, 614, 468, 614, 468, 644, 298, 644 ], "score": 0.431 }, { "category_id": 1, "poly": [ 298, 1408, 458, 1408, 458, 1438, 298, 1438 ], "score": 0.216 }, { "category_id": 1, "poly": [ 299, 1134, 468, 1134, 468, 1164, 299, 1164 ], "score": 0.209 }, { "category_id": 1, "poly": [ 298, 804, 458, 804, 458, 834, 298, 834 ], "score": 0.204 }, { "category_id": 13, "poly": [ 541, 400, 583, 400, 583, 421, 541, 421 ], "score": 0.75, "latex": "= =" }, { "category_id": 15, "poly": [ 292.0, 227.0, 408.0, 227.0, 408.0, 269.0, 292.0, 269.0 ], "score": 1.0, "text": "Prompts" }, { "category_id": 15, "poly": [ 295.0, 73.0, 817.0, 73.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.992, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 831.0, 2085.0, 870.0, 2085.0, 870.0, 2121.0, 831.0, 2121.0 ], "score": 1.0, "text": "83" }, { "category_id": 15, "poly": [ 293.0, 1679.0, 474.0, 1679.0, 474.0, 1717.0, 293.0, 1717.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1131.0, 474.0, 1131.0, 474.0, 1169.0, 293.0, 1169.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 802.0, 463.0, 802.0, 463.0, 838.0, 295.0, 838.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1406.0, 463.0, 1406.0, 463.0, 1442.0, 295.0, 1442.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 611.0, 474.0, 611.0, 474.0, 649.0, 293.0, 649.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 305.0, 393.0, 540.0, 393.0, 540.0, 427.0, 305.0, 427.0 ], "score": 0.997, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 584.0, 393.0, 698.0, 393.0, 698.0, 427.0, 584.0, 427.0 ], "score": 0.997, "text": "false%}" }, { "category_id": 15, "poly": [ 302.0, 420.0, 1058.0, 420.0, 1058.0, 455.0, 302.0, 455.0 ], "score": 0.978, "text": "Generate a question that has the following answer:" }, { "category_id": 15, "poly": [ 305.0, 449.0, 581.0, 449.0, 581.0, 482.0, 305.0, 482.0 ], "score": 0.979, "text": "{{answers|choice}}" }, { "category_id": 15, "poly": [ 302.0, 476.0, 744.0, 476.0, 744.0, 511.0, 302.0, 511.0 ], "score": 0.987, "text": "for the following movie plot:" }, { "category_id": 15, "poly": [ 304.0, 504.0, 432.0, 504.0, 432.0, 539.0, 304.0, 539.0 ], "score": 0.998, "text": "{{plot}}" }, { "category_id": 15, "poly": [ 301.0, 970.0, 623.0, 970.0, 623.0, 1001.0, 301.0, 1001.0 ], "score": 0.974, "text": "Plot title: {{title}}" }, { "category_id": 15, "poly": [ 302.0, 999.0, 610.0, 999.0, 610.0, 1030.0, 302.0, 1030.0 ], "score": 0.965, "text": "Movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 298.0, 1024.0, 684.0, 1024.0, 684.0, 1059.0, 298.0, 1059.0 ], "score": 0.973, "text": "My question: {{question}}" }, { "category_id": 15, "poly": [ 301.0, 1460.0, 1393.0, 1460.0, 1393.0, 1495.0, 301.0, 1495.0 ], "score": 0.993, "text": "Extract the answer to the following question from the movie plot. If the" }, { "category_id": 15, "poly": [ 301.0, 1488.0, 1232.0, 1488.0, 1232.0, 1522.0, 301.0, 1522.0 ], "score": 0.99, "text": "question isn't answerable, please output \"{{\"Can't answer\"}}\"." }, { "category_id": 15, "poly": [ 302.0, 1517.0, 640.0, 1517.0, 640.0, 1553.0, 302.0, 1553.0 ], "score": 0.99, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 301.0, 1544.0, 549.0, 1544.0, 549.0, 1579.0, 301.0, 1579.0 ], "score": 0.994, "text": "Title:{{title}}" }, { "category_id": 15, "poly": [ 300.0, 1569.0, 609.0, 1569.0, 609.0, 1609.0, 300.0, 1609.0 ], "score": 0.971, "text": "Movie plot:{{plot}}" }, { "category_id": 15, "poly": [ 307.0, 1738.0, 538.0, 1738.0, 538.0, 1765.0, 307.0, 1765.0 ], "score": 0.978, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 545.0, 1740.0, 576.0, 1740.0, 576.0, 1765.0, 545.0, 1765.0 ], "score": 0.945, "text": "%}" }, { "category_id": 15, "poly": [ 304.0, 1765.0, 492.0, 1765.0, 492.0, 1794.0, 304.0, 1794.0 ], "score": 0.996, "text": "Can't answer" }, { "category_id": 15, "poly": [ 306.0, 1791.0, 460.0, 1791.0, 460.0, 1824.0, 306.0, 1824.0 ], "score": 0.952, "text": "{% else %}" }, { "category_id": 15, "poly": [ 305.0, 1820.0, 622.0, 1820.0, 622.0, 1852.0, 305.0, 1852.0 ], "score": 0.962, "text": "{{answers 丨 choice }}" }, { "category_id": 15, "poly": [ 304.0, 1844.0, 476.0, 1844.0, 476.0, 1880.0, 304.0, 1880.0 ], "score": 0.958, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 307.0, 1190.0, 539.0, 1190.0, 539.0, 1217.0, 307.0, 1217.0 ], "score": 0.977, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 545.0, 1194.0, 575.0, 1194.0, 575.0, 1215.0, 545.0, 1215.0 ], "score": 0.985, "text": "%}" }, { "category_id": 15, "poly": [ 303.0, 1217.0, 521.0, 1217.0, 521.0, 1246.0, 303.0, 1246.0 ], "score": 0.997, "text": "Not answerable" }, { "category_id": 15, "poly": [ 308.0, 1245.0, 458.0, 1245.0, 458.0, 1276.0, 308.0, 1276.0 ], "score": 0.968, "text": "{% else %}" }, { "category_id": 15, "poly": [ 308.0, 1273.0, 578.0, 1273.0, 578.0, 1303.0, 308.0, 1303.0 ], "score": 0.986, "text": "{{answers|choice}}" }, { "category_id": 15, "poly": [ 307.0, 1300.0, 474.0, 1300.0, 474.0, 1330.0, 307.0, 1330.0 ], "score": 0.947, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 294.0, 305.0, 933.0, 305.0, 933.0, 343.0, 294.0, 343.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 341.0, 462.0, 341.0, 462.0, 373.0, 296.0, 373.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1951.0, 932.0, 1951.0, 932.0, 1989.0, 294.0, 1989.0 ], "score": 0.984, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 1987.0, 461.0, 1987.0, 461.0, 2019.0, 295.0, 2019.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 300.0, 857.0, 1313.0, 857.0, 1313.0, 893.0, 300.0, 893.0 ], "score": 0.984, "text": " I am a movie director and I just received the following movie plot." }, { "category_id": 15, "poly": [ 300.0, 885.0, 1361.0, 885.0, 1361.0, 923.0, 300.0, 923.0 ], "score": 0.993, "text": "Could you help me answer this question? If not， let me know by writing" }, { "category_id": 15, "poly": [ 302.0, 915.0, 643.0, 915.0, 643.0, 947.0, 302.0, 947.0 ], "score": 0.979, "text": "\"{{\"Not answerable\"}}\"" }, { "category_id": 15, "poly": [ 306.0, 668.0, 488.0, 668.0, 488.0, 702.0, 306.0, 702.0 ], "score": 0.999, "text": "{{question}}" }, { "category_id": 15, "poly": [ 305.0, 697.0, 476.0, 697.0, 476.0, 728.0, 305.0, 728.0 ], "score": 0.973, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 293.0, 611.0, 474.0, 611.0, 474.0, 649.0, 293.0, 649.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1406.0, 463.0, 1406.0, 463.0, 1442.0, 295.0, 1442.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1131.0, 474.0, 1131.0, 474.0, 1169.0, 293.0, 1169.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 802.0, 463.0, 802.0, 463.0, 838.0, 295.0, 838.0 ], "score": 0.999, "text": "Input Template:" } ], "page_info": { "page_no": 82, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 305, 1599, 1329, 1599, 1329, 1738, 305, 1738 ], "score": 0.956 }, { "category_id": 1, "poly": [ 305, 891, 621, 891, 621, 1032, 305, 1032 ], "score": 0.953 }, { "category_id": 1, "poly": [ 304, 667, 710, 667, 710, 752, 304, 752 ], "score": 0.95 }, { "category_id": 1, "poly": [ 297, 1113, 930, 1113, 930, 1173, 297, 1173 ], "score": 0.924 }, { "category_id": 1, "poly": [ 305, 1203, 1284, 1203, 1284, 1261, 305, 1261 ], "score": 0.902 }, { "category_id": 1, "poly": [ 301, 584, 1312, 584, 1312, 640, 301, 640 ], "score": 0.865 }, { "category_id": 1, "poly": [ 309, 1401, 470, 1401, 470, 1457, 309, 1457 ], "score": 0.86 }, { "category_id": 0, "poly": [ 299, 1820, 468, 1820, 468, 1849, 299, 1849 ], "score": 0.858 }, { "category_id": 0, "poly": [ 298, 1538, 458, 1538, 458, 1568, 298, 1568 ], "score": 0.826 }, { "category_id": 1, "poly": [ 303, 242, 1234, 242, 1234, 275, 303, 275 ], "score": 0.819 }, { "category_id": 0, "poly": [ 298, 355, 468, 355, 468, 385, 298, 385 ], "score": 0.818 }, { "category_id": 2, "poly": [ 834, 2089, 865, 2089, 865, 2113, 834, 2113 ], "score": 0.81 }, { "category_id": 2, "poly": [ 298, 74, 816, 74, 816, 106, 298, 106 ], "score": 0.807 }, { "category_id": 1, "poly": [ 307, 413, 514, 413, 514, 445, 307, 445 ], "score": 0.77 }, { "category_id": 0, "poly": [ 299, 1341, 468, 1341, 468, 1371, 299, 1371 ], "score": 0.754 }, { "category_id": 0, "poly": [ 299, 833, 468, 833, 468, 863, 299, 863 ], "score": 0.655 }, { "category_id": 0, "poly": [ 298, 524, 458, 524, 458, 554, 298, 554 ], "score": 0.631 }, { "category_id": 1, "poly": [ 306, 1878, 993, 1878, 993, 2018, 306, 2018 ], "score": 0.476 }, { "category_id": 1, "poly": [ 298, 524, 458, 524, 458, 554, 298, 554 ], "score": 0.292 }, { "category_id": 1, "poly": [ 299, 833, 468, 833, 468, 863, 299, 863 ], "score": 0.266 }, { "category_id": 1, "poly": [ 299, 1341, 468, 1341, 468, 1371, 299, 1371 ], "score": 0.154 }, { "category_id": 2, "poly": [ 834, 2089, 864, 2089, 864, 2113, 834, 2113 ], "score": 0.121 }, { "category_id": 13, "poly": [ 541, 1206, 583, 1206, 583, 1228, 541, 1228 ], "score": 0.76, "latex": "= =" }, { "category_id": 15, "poly": [ 294.0, 1816.0, 474.0, 1816.0, 474.0, 1855.0, 294.0, 1855.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1536.0, 463.0, 1536.0, 463.0, 1572.0, 295.0, 1572.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 352.0, 474.0, 352.0, 474.0, 390.0, 293.0, 390.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 871.0, 2085.0, 871.0, 2121.0, 830.0, 2121.0 ], "score": 1.0, "text": "84" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 1338.0, 474.0, 1338.0, 474.0, 1376.0, 293.0, 1376.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 830.0, 474.0, 830.0, 474.0, 868.0, 293.0, 868.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 522.0, 463.0, 522.0, 463.0, 558.0, 295.0, 558.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 831.0, 2085.0, 870.0, 2085.0, 870.0, 2122.0, 831.0, 2122.0 ], "score": 1.0, "text": "84" }, { "category_id": 15, "poly": [ 302.0, 1595.0, 640.0, 1595.0, 640.0, 1629.0, 302.0, 1629.0 ], "score": 0.978, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 1619.0, 1331.0, 1619.0, 1331.0, 1657.0, 302.0, 1657.0 ], "score": 0.986, "text": "If there is no answer, please output \"{{\"Insufficient information to" }, { "category_id": 15, "poly": [ 298.0, 1647.0, 646.0, 1647.0, 646.0, 1685.0, 298.0, 1685.0 ], "score": 0.983, "text": " provide an answer.\"}}\"." }, { "category_id": 15, "poly": [ 300.0, 1678.0, 640.0, 1678.0, 640.0, 1712.0, 300.0, 1712.0 ], "score": 0.955, "text": "Movie title: {{title}}" }, { "category_id": 15, "poly": [ 301.0, 1706.0, 565.0, 1706.0, 565.0, 1741.0, 301.0, 1741.0 ], "score": 0.997, "text": "Context:{{plot}}" }, { "category_id": 15, "poly": [ 307.0, 892.0, 541.0, 892.0, 541.0, 919.0, 307.0, 919.0 ], "score": 0.968, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 545.0, 895.0, 574.0, 895.0, 574.0, 917.0, 545.0, 917.0 ], "score": 0.973, "text": "%}" }, { "category_id": 15, "poly": [ 301.0, 920.0, 447.0, 920.0, 447.0, 949.0, 301.0, 949.0 ], "score": 0.999, "text": "No answer" }, { "category_id": 15, "poly": [ 304.0, 945.0, 455.0, 945.0, 455.0, 977.0, 304.0, 977.0 ], "score": 0.897, "text": "{% else %}" }, { "category_id": 15, "poly": [ 306.0, 974.0, 598.0, 974.0, 598.0, 1006.0, 306.0, 1006.0 ], "score": 0.949, "text": "{{answers I choice]" }, { "category_id": 15, "poly": [ 304.0, 998.0, 476.0, 998.0, 476.0, 1034.0, 304.0, 1034.0 ], "score": 0.958, "text": "{% endif %}" }, { "category_id": 16, "poly": [ 582.0, 979.0, 619.0, 979.0, 619.0, 1002.5, 582.0, 1002.5 ], "score": 0.387, "text": "J" }, { "category_id": 15, "poly": [ 303.0, 666.0, 637.0, 666.0, 637.0, 697.0, 303.0, 697.0 ], "score": 0.986, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 694.0, 713.0, 694.0, 713.0, 725.0, 302.0, 725.0 ], "score": 0.969, "text": "Movie plot title: {{title}}" }, { "category_id": 15, "poly": [ 300.0, 720.0, 608.0, 720.0, 608.0, 754.0, 300.0, 754.0 ], "score": 0.964, "text": "Movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 294.0, 1110.0, 932.0, 1110.0, 932.0, 1146.0, 294.0, 1146.0 ], "score": 0.997, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1144.0, 462.0, 1144.0, 462.0, 1176.0, 296.0, 1176.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 1200.0, 540.0, 1200.0, 540.0, 1233.0, 304.0, 1233.0 ], "score": 0.996, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 584.0, 1200.0, 699.0, 1200.0, 699.0, 1233.0, 584.0, 1233.0 ], "score": 0.995, "text": "false%}" }, { "category_id": 15, "poly": [ 302.0, 1227.0, 1282.0, 1227.0, 1282.0, 1262.0, 302.0, 1262.0 ], "score": 0.976, "text": "Build a movie plot around this: {{ question }} {{answerslchoice}}" }, { "category_id": 15, "poly": [ 300.0, 579.0, 1317.0, 579.0, 1317.0, 617.0, 300.0, 617.0 ], "score": 0.982, "text": " Please answer the following question about this movie plot. If it's" }, { "category_id": 15, "poly": [ 298.0, 607.0, 1014.0, 607.0, 1014.0, 642.0, 298.0, 642.0 ], "score": 0.962, "text": " un-answerable, please output \"\"{{\"No answer\"}}\"." }, { "category_id": 15, "poly": [ 305.0, 1397.0, 461.0, 1397.0, 461.0, 1432.0, 305.0, 1432.0 ], "score": 0.961, "text": "{{plot }}" }, { "category_id": 15, "poly": [ 304.0, 1425.0, 475.0, 1425.0, 475.0, 1460.0, 304.0, 1460.0 ], "score": 0.962, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 300.0, 240.0, 1238.0, 240.0, 1238.0, 277.0, 300.0, 277.0 ], "score": 0.987, "text": "Generate a question about the following movie plot: {{ plot }}" }, { "category_id": 15, "poly": [ 305.0, 409.0, 520.0, 409.0, 520.0, 449.0, 305.0, 449.0 ], "score": 0.984, "text": "{{ question }}" }, { "category_id": 15, "poly": [ 306.0, 1877.0, 539.0, 1877.0, 539.0, 1907.0, 306.0, 1907.0 ], "score": 0.963, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 544.0, 1878.0, 578.0, 1878.0, 578.0, 1906.0, 544.0, 1906.0 ], "score": 0.938, "text": "%}" }, { "category_id": 15, "poly": [ 303.0, 1905.0, 996.0, 1905.0, 996.0, 1936.0, 303.0, 1936.0 ], "score": 0.994, "text": "Insufficient information to provide an answer." }, { "category_id": 15, "poly": [ 306.0, 1932.0, 461.0, 1932.0, 461.0, 1965.0, 306.0, 1965.0 ], "score": 0.942, "text": "{% else %}" }, { "category_id": 15, "poly": [ 305.0, 1959.0, 580.0, 1959.0, 580.0, 1994.0, 305.0, 1994.0 ], "score": 0.978, "text": "{{answers|choice}}" }, { "category_id": 15, "poly": [ 306.0, 1984.0, 477.0, 1984.0, 477.0, 2020.0, 306.0, 2020.0 ], "score": 0.978, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 522.0, 463.0, 522.0, 463.0, 558.0, 295.0, 558.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 830.0, 474.0, 830.0, 474.0, 868.0, 293.0, 868.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1338.0, 474.0, 1338.0, 474.0, 1376.0, 293.0, 1376.0 ], "score": 0.998, "text": "Target Template:" } ], "page_info": { "page_no": 83, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 297, 1480, 1300, 1480, 1300, 1738, 297, 1738 ], "score": 0.976, "html": "
KeyValue
answers['second in command Sergeant Jericho and prisoner...
no_answerFalse
plotSet in the second half of the 22nd century，Mars h...
plot_id/m/03vyhn
question question_idwho is there with Melanie Ballard?
28ded42d-f6d5-aac6-cf6f-9e6e0820c5aa
titleGhosts of Mars
" }, { "category_id": 1, "poly": [ 304, 800, 636, 800, 636, 885, 304, 885 ], "score": 0.955 }, { "category_id": 1, "poly": [ 305, 1022, 576, 1022, 576, 1163, 305, 1163 ], "score": 0.951 }, { "category_id": 1, "poly": [ 306, 688, 1362, 688, 1362, 773, 306, 773 ], "score": 0.947 }, { "category_id": 1, "poly": [ 297, 1872, 931, 1872, 931, 1933, 297, 1933 ], "score": 0.94 }, { "category_id": 1, "poly": [ 297, 262, 931, 262, 931, 323, 297, 323 ], "score": 0.917 }, { "category_id": 0, "poly": [ 301, 1276, 682, 1276, 682, 1309, 301, 1309 ], "score": 0.914 }, { "category_id": 1, "poly": [ 300, 1329, 847, 1329, 847, 1363, 300, 1363 ], "score": 0.911 }, { "category_id": 0, "poly": [ 297, 1793, 403, 1793, 403, 1825, 297, 1825 ], "score": 0.903 }, { "category_id": 1, "poly": [ 303, 349, 1203, 349, 1203, 381, 303, 381 ], "score": 0.872 }, { "category_id": 2, "poly": [ 834, 2089, 864, 2089, 864, 2113, 834, 2113 ], "score": 0.854 }, { "category_id": 2, "poly": [ 299, 75, 815, 75, 815, 105, 299, 105 ], "score": 0.846 }, { "category_id": 1, "poly": [ 303, 1958, 1292, 1958, 1292, 2021, 303, 2021 ], "score": 0.842 }, { "category_id": 0, "poly": [ 299, 965, 467, 965, 467, 995, 299, 995 ], "score": 0.818 }, { "category_id": 0, "poly": [ 298, 630, 458, 630, 458, 660, 298, 660 ], "score": 0.766 }, { "category_id": 1, "poly": [ 308, 520, 440, 520, 440, 549, 308, 549 ], "score": 0.587 }, { "category_id": 1, "poly": [ 298, 462, 467, 462, 467, 492, 298, 492 ], "score": 0.581 }, { "category_id": 0, "poly": [ 298, 462, 467, 462, 467, 492, 298, 492 ], "score": 0.361 }, { "category_id": 0, "poly": [ 298, 1395, 470, 1395, 470, 1428, 298, 1428 ], "score": 0.337 }, { "category_id": 1, "poly": [ 298, 630, 458, 630, 458, 660, 298, 660 ], "score": 0.152 }, { "category_id": 1, "poly": [ 299, 965, 467, 965, 467, 995, 299, 995 ], "score": 0.096 }, { "category_id": 0, "poly": [ 308, 520, 440, 520, 440, 549, 308, 549 ], "score": 0.093 }, { "category_id": 13, "poly": [ 540, 1966, 583, 1966, 583, 1987, 540, 1987 ], "score": 0.73, "latex": "= =" }, { "category_id": 15, "poly": [ 297.0, 1275.0, 683.0, 1275.0, 683.0, 1311.0, 297.0, 1311.0 ], "score": 0.997, "text": "1.6.5DUORC PARAPHRASERC" }, { "category_id": 15, "poly": [ 292.0, 1790.0, 408.0, 1790.0, 408.0, 1832.0, 292.0, 1832.0 ], "score": 1.0, "text": "Prompts" }, { "category_id": 15, "poly": [ 831.0, 2085.0, 870.0, 2085.0, 870.0, 2121.0, 831.0, 2121.0 ], "score": 1.0, "text": "85" }, { "category_id": 15, "poly": [ 295.0, 73.0, 817.0, 73.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.992, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 294.0, 962.0, 473.0, 962.0, 473.0, 1000.0, 294.0, 1000.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 628.0, 463.0, 628.0, 463.0, 664.0, 295.0, 664.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 459.0, 474.0, 459.0, 474.0, 497.0, 293.0, 497.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1392.0, 473.0, 1392.0, 473.0, 1431.0, 294.0, 1431.0 ], "score": 0.999, "text": "Data Example" }, { "category_id": 15, "poly": [ 304.0, 516.0, 445.0, 516.0, 445.0, 552.0, 304.0, 552.0 ], "score": 0.998, "text": "{{title}}" }, { "category_id": 15, "poly": [ 302.0, 797.0, 638.0, 797.0, 638.0, 831.0, 302.0, 831.0 ], "score": 0.986, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 301.0, 826.0, 639.0, 826.0, 639.0, 857.0, 301.0, 857.0 ], "score": 0.997, "text": "Movie title:{{title}}" }, { "category_id": 15, "poly": [ 300.0, 852.0, 609.0, 852.0, 609.0, 886.0, 300.0, 886.0 ], "score": 0.955, "text": "Movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 308.0, 1023.0, 542.0, 1023.0, 542.0, 1050.0, 308.0, 1050.0 ], "score": 0.995, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 544.0, 1027.0, 574.0, 1027.0, 574.0, 1048.0, 544.0, 1048.0 ], "score": 0.97, "text": "%}" }, { "category_id": 15, "poly": [ 303.0, 1048.0, 462.0, 1048.0, 462.0, 1080.0, 303.0, 1080.0 ], "score": 0.985, "text": "No I can't" }, { "category_id": 15, "poly": [ 305.0, 1078.0, 459.0, 1078.0, 459.0, 1108.0, 305.0, 1108.0 ], "score": 0.973, "text": "{% else %}" }, { "category_id": 15, "poly": [ 308.0, 1106.0, 578.0, 1106.0, 578.0, 1136.0, 308.0, 1136.0 ], "score": 0.986, "text": "{{answers|choice}}" }, { "category_id": 15, "poly": [ 307.0, 1131.0, 475.0, 1131.0, 475.0, 1164.0, 307.0, 1164.0 ], "score": 0.988, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 299.0, 684.0, 1301.0, 684.0, 1301.0, 720.0, 299.0, 720.0 ], "score": 0.973, "text": " I am trying to decide whether it's worth it to invest in this film" }, { "category_id": 15, "poly": [ 298.0, 713.0, 1363.0, 713.0, 1363.0, 750.0, 298.0, 750.0 ], "score": 0.982, "text": " proposal. Can you help me answer a few questions? If you can't， please" }, { "category_id": 15, "poly": [ 304.0, 741.0, 645.0, 741.0, 645.0, 777.0, 304.0, 777.0 ], "score": 0.951, "text": "say \"{{\"No I can't\"}}\"." }, { "category_id": 15, "poly": [ 294.0, 1868.0, 933.0, 1868.0, 933.0, 1906.0, 294.0, 1906.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1904.0, 462.0, 1904.0, 462.0, 1936.0, 296.0, 1936.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 258.0, 933.0, 258.0, 933.0, 296.0, 294.0, 296.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 294.0, 462.0, 294.0, 462.0, 326.0, 296.0, 326.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1322.0, 852.0, 1322.0, 852.0, 1370.0, 293.0, 1370.0 ], "score": 0.981, "text": "Datase from Saha et al. (2018). Used in training." }, { "category_id": 15, "poly": [ 301.0, 349.0, 1207.0, 349.0, 1207.0, 384.0, 301.0, 384.0 ], "score": 0.989, "text": "Suggest a movie title for the following movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 306.0, 1960.0, 539.0, 1960.0, 539.0, 1992.0, 306.0, 1992.0 ], "score": 0.988, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 584.0, 1960.0, 697.0, 1960.0, 697.0, 1992.0, 584.0, 1992.0 ], "score": 0.994, "text": "false%}" }, { "category_id": 15, "poly": [ 301.0, 1986.0, 1281.0, 1986.0, 1281.0, 2021.0, 301.0, 2021.0 ], "score": 0.99, "text": "Build a movie plot around this: {{ question }} {{answers|choice}}" }, { "category_id": 15, "poly": [ 304.0, 516.0, 445.0, 516.0, 445.0, 552.0, 304.0, 552.0 ], "score": 0.998, "text": "{{title}}" }, { "category_id": 15, "poly": [ 293.0, 459.0, 474.0, 459.0, 474.0, 497.0, 293.0, 497.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 628.0, 463.0, 628.0, 463.0, 664.0, 295.0, 664.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 962.0, 473.0, 962.0, 473.0, 1000.0, 294.0, 1000.0 ], "score": 0.999, "text": "Target Template:" } ], "page_info": { "page_no": 84, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 306, 1136, 1329, 1136, 1329, 1280, 306, 1280 ], "score": 0.957 }, { "category_id": 1, "poly": [ 303, 631, 636, 631, 636, 717, 303, 717 ], "score": 0.955 }, { "category_id": 1, "poly": [ 306, 1417, 993, 1417, 993, 1557, 306, 1557 ], "score": 0.953 }, { "category_id": 1, "poly": [ 304, 1809, 680, 1809, 680, 1894, 304, 1894 ], "score": 0.953 }, { "category_id": 1, "poly": [ 305, 856, 576, 856, 576, 996, 305, 996 ], "score": 0.947 }, { "category_id": 1, "poly": [ 305, 518, 1361, 518, 1361, 603, 305, 603 ], "score": 0.945 }, { "category_id": 1, "poly": [ 304, 1697, 1363, 1697, 1363, 1781, 304, 1781 ], "score": 0.931 }, { "category_id": 1, "poly": [ 308, 323, 470, 323, 470, 379, 308, 379 ], "score": 0.876 }, { "category_id": 2, "poly": [ 299, 75, 816, 75, 816, 105, 299, 105 ], "score": 0.869 }, { "category_id": 2, "poly": [ 834, 2089, 865, 2089, 865, 2114, 834, 2114 ], "score": 0.855 }, { "category_id": 0, "poly": [ 298, 1638, 458, 1638, 458, 1668, 298, 1668 ], "score": 0.849 }, { "category_id": 0, "poly": [ 299, 1358, 467, 1358, 467, 1388, 299, 1388 ], "score": 0.833 }, { "category_id": 0, "poly": [ 298, 263, 468, 263, 468, 293, 298, 293 ], "score": 0.805 }, { "category_id": 0, "poly": [ 299, 796, 467, 796, 467, 826, 299, 826 ], "score": 0.801 }, { "category_id": 0, "poly": [ 298, 1077, 458, 1077, 458, 1107, 298, 1107 ], "score": 0.748 }, { "category_id": 0, "poly": [ 298, 460, 458, 460, 458, 490, 298, 490 ], "score": 0.66 }, { "category_id": 1, "poly": [ 299, 1975, 468, 1975, 468, 2005, 299, 2005 ], "score": 0.565 }, { "category_id": 1, "poly": [ 298, 460, 458, 460, 458, 490, 298, 490 ], "score": 0.266 }, { "category_id": 0, "poly": [ 299, 1975, 468, 1975, 468, 2005, 299, 2005 ], "score": 0.25 }, { "category_id": 1, "poly": [ 298, 1077, 458, 1077, 458, 1107, 298, 1107 ], "score": 0.162 }, { "category_id": 1, "poly": [ 299, 796, 467, 796, 467, 826, 299, 826 ], "score": 0.105 }, { "category_id": 1, "poly": [ 298, 263, 468, 263, 468, 293, 298, 293 ], "score": 0.099 }, { "category_id": 15, "poly": [ 295.0, 73.0, 817.0, 73.0, 817.0, 108.0, 295.0, 108.0 ], "score": 0.992, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 830.0, 2085.0, 871.0, 2085.0, 871.0, 2120.0, 830.0, 2120.0 ], "score": 1.0, "text": "86" }, { "category_id": 15, "poly": [ 295.0, 1636.0, 463.0, 1636.0, 463.0, 1672.0, 295.0, 1672.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1355.0, 473.0, 1355.0, 473.0, 1393.0, 294.0, 1393.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 260.0, 474.0, 260.0, 474.0, 298.0, 293.0, 298.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 793.0, 473.0, 793.0, 473.0, 831.0, 294.0, 831.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1075.0, 463.0, 1075.0, 463.0, 1111.0, 295.0, 1111.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 458.0, 463.0, 458.0, 463.0, 494.0, 295.0, 494.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1971.0, 474.0, 1971.0, 474.0, 2009.0, 294.0, 2009.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 304.0, 1134.0, 639.0, 1134.0, 639.0, 1166.0, 304.0, 1166.0 ], "score": 0.985, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 1158.0, 1332.0, 1158.0, 1332.0, 1198.0, 302.0, 1198.0 ], "score": 0.995, "text": "If there is no answer, please output \"{{\"Insufficient information to" }, { "category_id": 15, "poly": [ 299.0, 1190.0, 645.0, 1190.0, 645.0, 1221.0, 299.0, 1221.0 ], "score": 0.998, "text": "provide an answer.\"}}\"." }, { "category_id": 15, "poly": [ 302.0, 1218.0, 641.0, 1218.0, 641.0, 1250.0, 302.0, 1250.0 ], "score": 0.976, "text": "Movie title: {{title}}" }, { "category_id": 15, "poly": [ 303.0, 1246.0, 564.0, 1246.0, 564.0, 1278.0, 303.0, 1278.0 ], "score": 0.999, "text": "Context:{{plot}}" }, { "category_id": 15, "poly": [ 303.0, 629.0, 638.0, 629.0, 638.0, 660.0, 303.0, 660.0 ], "score": 0.983, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 300.0, 654.0, 640.0, 654.0, 640.0, 690.0, 300.0, 690.0 ], "score": 0.978, "text": "Movie title: {{title}}" }, { "category_id": 15, "poly": [ 300.0, 685.0, 609.0, 685.0, 609.0, 719.0, 300.0, 719.0 ], "score": 0.953, "text": "Movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 307.0, 1418.0, 539.0, 1418.0, 539.0, 1445.0, 307.0, 1445.0 ], "score": 0.991, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 544.0, 1417.0, 578.0, 1417.0, 578.0, 1445.0, 544.0, 1445.0 ], "score": 0.938, "text": "%}" }, { "category_id": 15, "poly": [ 303.0, 1444.0, 996.0, 1444.0, 996.0, 1475.0, 303.0, 1475.0 ], "score": 0.994, "text": "Insufficient information to provide an answer." }, { "category_id": 15, "poly": [ 306.0, 1470.0, 462.0, 1470.0, 462.0, 1503.0, 306.0, 1503.0 ], "score": 0.946, "text": "{% else %}" }, { "category_id": 15, "poly": [ 305.0, 1497.0, 580.0, 1497.0, 580.0, 1532.0, 305.0, 1532.0 ], "score": 0.978, "text": "{{answers|choice}}" }, { "category_id": 15, "poly": [ 306.0, 1523.0, 477.0, 1523.0, 477.0, 1559.0, 306.0, 1559.0 ], "score": 0.975, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 300.0, 1806.0, 624.0, 1806.0, 624.0, 1840.0, 300.0, 1840.0 ], "score": 0.997, "text": "Plot title:{{title}}" }, { "category_id": 15, "poly": [ 302.0, 1836.0, 610.0, 1836.0, 610.0, 1867.0, 302.0, 1867.0 ], "score": 0.964, "text": "Movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 300.0, 1860.0, 684.0, 1860.0, 684.0, 1897.0, 300.0, 1897.0 ], "score": 0.983, "text": "My question: {{question}}" }, { "category_id": 15, "poly": [ 306.0, 854.0, 579.0, 854.0, 579.0, 886.0, 306.0, 886.0 ], "score": 0.946, "text": "{% if no_answer %}" }, { "category_id": 15, "poly": [ 303.0, 883.0, 462.0, 883.0, 462.0, 913.0, 303.0, 913.0 ], "score": 0.998, "text": "No I can't" }, { "category_id": 15, "poly": [ 306.0, 909.0, 460.0, 909.0, 460.0, 942.0, 306.0, 942.0 ], "score": 0.952, "text": "{% else %}" }, { "category_id": 15, "poly": [ 308.0, 939.0, 578.0, 939.0, 578.0, 969.0, 308.0, 969.0 ], "score": 0.986, "text": "{{answers|choice}}" }, { "category_id": 15, "poly": [ 307.0, 964.0, 475.0, 964.0, 475.0, 997.0, 307.0, 997.0 ], "score": 0.988, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 300.0, 515.0, 1301.0, 515.0, 1301.0, 551.0, 300.0, 551.0 ], "score": 0.98, "text": "I am trying to decide whether it's worth it to invest in this film" }, { "category_id": 15, "poly": [ 300.0, 545.0, 1363.0, 545.0, 1363.0, 580.0, 300.0, 580.0 ], "score": 0.975, "text": " proposal. Can you help me answer a few questions? If you can't， please" }, { "category_id": 15, "poly": [ 303.0, 576.0, 359.0, 576.0, 359.0, 608.0, 303.0, 608.0 ], "score": 0.999, "text": "say" }, { "category_id": 15, "poly": [ 371.0, 574.0, 466.0, 574.0, 466.0, 604.0, 371.0, 604.0 ], "score": 0.922, "text": "\"{{\"No" }, { "category_id": 15, "poly": [ 467.0, 581.0, 491.0, 581.0, 491.0, 599.0, 467.0, 599.0 ], "score": 0.622, "text": "工" }, { "category_id": 15, "poly": [ 498.0, 579.0, 583.0, 579.0, 583.0, 602.0, 498.0, 602.0 ], "score": 0.92, "text": "can't'" }, { "category_id": 16, "poly": [ 595.0, 586.0, 604.0, 586.0, 604.0, 594.0, 595.0, 594.0 ], "score": 0.173, "text": "，" }, { "category_id": 15, "poly": [ 613.0, 577.0, 642.0, 577.0, 642.0, 599.0, 613.0, 599.0 ], "score": 0.797, "text": "\"" }, { "category_id": 15, "poly": [ 300.0, 1694.0, 1312.0, 1694.0, 1312.0, 1730.0, 300.0, 1730.0 ], "score": 0.994, "text": "I am a movie director and I just received the following movie plot." }, { "category_id": 15, "poly": [ 300.0, 1722.0, 1362.0, 1722.0, 1362.0, 1760.0, 300.0, 1760.0 ], "score": 0.982, "text": "Could you help me answer this question? If not， let me know by writing" }, { "category_id": 15, "poly": [ 302.0, 1750.0, 624.0, 1750.0, 624.0, 1785.0, 302.0, 1785.0 ], "score": 0.997, "text": "\"{{\"Not answerable\"}}" }, { "category_id": 15, "poly": [ 609.75, 1757.0, 641.75, 1757.0, 641.75, 1774.0, 609.75, 1774.0 ], "score": 0.687, "text": "\"" }, { "category_id": 15, "poly": [ 304.0, 319.0, 463.0, 319.0, 463.0, 354.0, 304.0, 354.0 ], "score": 0.953, "text": "{{ plot }}" }, { "category_id": 15, "poly": [ 303.0, 347.0, 475.0, 347.0, 475.0, 382.0, 303.0, 382.0 ], "score": 0.953, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 294.0, 1971.0, 474.0, 1971.0, 474.0, 2009.0, 294.0, 2009.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 458.0, 463.0, 458.0, 463.0, 494.0, 295.0, 494.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1075.0, 463.0, 1075.0, 463.0, 1111.0, 295.0, 1111.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 793.0, 473.0, 793.0, 473.0, 831.0, 294.0, 831.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 260.0, 474.0, 260.0, 474.0, 298.0, 293.0, 298.0 ], "score": 0.998, "text": "Target Template:" } ], "page_info": { "page_no": 85, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 304, 904, 1396, 904, 1396, 1049, 304, 1049 ], "score": 0.949 }, { "category_id": 1, "poly": [ 305, 1189, 622, 1189, 622, 1329, 305, 1329 ], "score": 0.947 }, { "category_id": 1, "poly": [ 304, 243, 576, 243, 576, 383, 304, 383 ], "score": 0.938 }, { "category_id": 1, "poly": [ 304, 1935, 708, 1935, 708, 2020, 304, 2020 ], "score": 0.936 }, { "category_id": 1, "poly": [ 297, 1412, 931, 1412, 931, 1473, 297, 1473 ], "score": 0.92 }, { "category_id": 1, "poly": [ 304, 1503, 1203, 1503, 1203, 1535, 304, 1535 ], "score": 0.868 }, { "category_id": 1, "poly": [ 305, 557, 1234, 557, 1234, 589, 305, 589 ], "score": 0.865 }, { "category_id": 1, "poly": [ 296, 466, 930, 466, 930, 527, 296, 527 ], "score": 0.861 }, { "category_id": 0, "poly": [ 298, 671, 468, 671, 468, 701, 298, 701 ], "score": 0.783 }, { "category_id": 1, "poly": [ 309, 732, 515, 732, 515, 762, 309, 762 ], "score": 0.764 }, { "category_id": 0, "poly": [ 299, 1129, 468, 1129, 468, 1158, 299, 1158 ], "score": 0.746 }, { "category_id": 2, "poly": [ 834, 2089, 864, 2089, 864, 2113, 834, 2113 ], "score": 0.733 }, { "category_id": 0, "poly": [ 298, 1791, 458, 1791, 458, 1820, 298, 1820 ], "score": 0.729 }, { "category_id": 1, "poly": [ 301, 1852, 1312, 1852, 1312, 1907, 301, 1907 ], "score": 0.726 }, { "category_id": 2, "poly": [ 298, 74, 816, 74, 816, 106, 298, 106 ], "score": 0.701 }, { "category_id": 1, "poly": [ 298, 844, 458, 844, 458, 874, 298, 874 ], "score": 0.596 }, { "category_id": 0, "poly": [ 298, 1617, 468, 1617, 468, 1647, 298, 1647 ], "score": 0.492 }, { "category_id": 1, "poly": [ 298, 1617, 468, 1617, 468, 1647, 298, 1647 ], "score": 0.432 }, { "category_id": 1, "poly": [ 307, 1678, 440, 1678, 440, 1708, 307, 1708 ], "score": 0.344 }, { "category_id": 0, "poly": [ 298, 844, 458, 844, 458, 874, 298, 874 ], "score": 0.299 }, { "category_id": 2, "poly": [ 834, 2089, 864, 2089, 864, 2113, 834, 2113 ], "score": 0.26 }, { "category_id": 1, "poly": [ 298, 74, 816, 74, 816, 106, 298, 106 ], "score": 0.182 }, { "category_id": 1, "poly": [ 299, 1129, 468, 1129, 468, 1158, 299, 1158 ], "score": 0.171 }, { "category_id": 1, "poly": [ 298, 1791, 458, 1791, 458, 1820, 298, 1820 ], "score": 0.161 }, { "category_id": 1, "poly": [ 298, 671, 468, 671, 468, 701, 298, 701 ], "score": 0.112 }, { "category_id": 15, "poly": [ 293.0, 668.0, 474.0, 668.0, 474.0, 706.0, 293.0, 706.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1125.0, 474.0, 1125.0, 474.0, 1164.0, 294.0, 1164.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 831.0, 2085.0, 869.0, 2085.0, 869.0, 2122.0, 831.0, 2122.0 ], "score": 1.0, "text": "87" }, { "category_id": 15, "poly": [ 295.0, 1788.0, 463.0, 1788.0, 463.0, 1825.0, 295.0, 1825.0 ], "score": 0.971, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 1614.0, 474.0, 1614.0, 474.0, 1652.0, 293.0, 1652.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 842.0, 463.0, 842.0, 463.0, 878.0, 295.0, 878.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 831.0, 2085.0, 869.0, 2085.0, 869.0, 2122.0, 831.0, 2122.0 ], "score": 1.0, "text": "87" }, { "category_id": 15, "poly": [ 300.0, 900.0, 1393.0, 900.0, 1393.0, 936.0, 300.0, 936.0 ], "score": 0.999, "text": "Extract the answer to the following question from the movie plot. If the" }, { "category_id": 15, "poly": [ 301.0, 931.0, 1229.0, 931.0, 1229.0, 962.0, 301.0, 962.0 ], "score": 0.991, "text": "question isn't answerable， please output \"{{\"Can't answer\"}}\"." }, { "category_id": 15, "poly": [ 302.0, 956.0, 639.0, 956.0, 639.0, 992.0, 302.0, 992.0 ], "score": 0.988, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 301.0, 984.0, 550.0, 984.0, 550.0, 1020.0, 301.0, 1020.0 ], "score": 0.995, "text": "Title:{{title}}" }, { "category_id": 15, "poly": [ 299.0, 1011.0, 609.0, 1011.0, 609.0, 1051.0, 299.0, 1051.0 ], "score": 0.967, "text": "Movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 307.0, 1191.0, 537.0, 1191.0, 537.0, 1217.0, 307.0, 1217.0 ], "score": 0.994, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 545.0, 1191.0, 576.0, 1191.0, 576.0, 1217.0, 545.0, 1217.0 ], "score": 0.931, "text": "%}" }, { "category_id": 15, "poly": [ 304.0, 1217.0, 492.0, 1217.0, 492.0, 1245.0, 304.0, 1245.0 ], "score": 0.998, "text": "Can't answer" }, { "category_id": 15, "poly": [ 307.0, 1244.0, 459.0, 1244.0, 459.0, 1274.0, 307.0, 1274.0 ], "score": 0.915, "text": "{% else %}" }, { "category_id": 15, "poly": [ 304.0, 1269.0, 623.0, 1269.0, 623.0, 1305.0, 304.0, 1305.0 ], "score": 0.978, "text": "{{answers丨 choice }}" }, { "category_id": 15, "poly": [ 303.0, 1297.0, 476.0, 1297.0, 476.0, 1330.0, 303.0, 1330.0 ], "score": 0.966, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 308.0, 245.0, 539.0, 245.0, 539.0, 272.0, 308.0, 272.0 ], "score": 0.997, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 545.0, 247.0, 575.0, 247.0, 575.0, 269.0, 545.0, 269.0 ], "score": 0.979, "text": "%}" }, { "category_id": 15, "poly": [ 303.0, 273.0, 521.0, 273.0, 521.0, 299.0, 303.0, 299.0 ], "score": 0.975, "text": "Not answerable" }, { "category_id": 15, "poly": [ 309.0, 300.0, 457.0, 300.0, 457.0, 327.0, 309.0, 327.0 ], "score": 0.952, "text": "{% else %}" }, { "category_id": 15, "poly": [ 307.0, 325.0, 578.0, 325.0, 578.0, 357.0, 307.0, 357.0 ], "score": 0.981, "text": "{{answers|choice}}" }, { "category_id": 15, "poly": [ 306.0, 351.0, 475.0, 351.0, 475.0, 384.0, 306.0, 384.0 ], "score": 0.986, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 303.0, 1934.0, 638.0, 1934.0, 638.0, 1965.0, 303.0, 1965.0 ], "score": 0.988, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 301.0, 1961.0, 714.0, 1961.0, 714.0, 1995.0, 301.0, 1995.0 ], "score": 0.991, "text": "Movie plot title:{{title}}" }, { "category_id": 15, "poly": [ 300.0, 1988.0, 609.0, 1988.0, 609.0, 2022.0, 300.0, 2022.0 ], "score": 0.98, "text": "Movie plot:{{plot}}" }, { "category_id": 15, "poly": [ 294.0, 1408.0, 933.0, 1408.0, 933.0, 1446.0, 294.0, 1446.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1444.0, 462.0, 1444.0, 462.0, 1476.0, 296.0, 1476.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 302.0, 1501.0, 1207.0, 1501.0, 1207.0, 1537.0, 302.0, 1537.0 ], "score": 0.999, "text": "Suggest a movie title for the following movie plot: {{plot}}" }, { "category_id": 15, "poly": [ 301.0, 553.0, 1238.0, 553.0, 1238.0, 592.0, 301.0, 592.0 ], "score": 0.993, "text": "Generate a question about the following movie plot: {{ plot }}" }, { "category_id": 15, "poly": [ 294.0, 462.0, 932.0, 462.0, 932.0, 500.0, 294.0, 500.0 ], "score": 0.994, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 498.0, 461.0, 498.0, 461.0, 530.0, 295.0, 530.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 728.0, 520.0, 728.0, 520.0, 766.0, 304.0, 766.0 ], "score": 0.947, "text": "{{ question }}" }, { "category_id": 15, "poly": [ 298.0, 1847.0, 1317.0, 1847.0, 1317.0, 1883.0, 298.0, 1883.0 ], "score": 0.987, "text": " Please answer the following question about this movie plot. If it's" }, { "category_id": 15, "poly": [ 300.0, 1873.0, 1014.0, 1873.0, 1014.0, 1910.0, 300.0, 1910.0 ], "score": 0.976, "text": " un-answerable， please output \"{{\"No answer\"}}\"." }, { "category_id": 15, "poly": [ 295.0, 842.0, 463.0, 842.0, 463.0, 878.0, 295.0, 878.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1614.0, 474.0, 1614.0, 474.0, 1652.0, 293.0, 1652.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 304.0, 1675.0, 445.0, 1675.0, 445.0, 1710.0, 304.0, 1710.0 ], "score": 0.998, "text": "{{title}}" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 294.0, 1125.0, 474.0, 1125.0, 474.0, 1164.0, 294.0, 1164.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1788.0, 463.0, 1788.0, 463.0, 1825.0, 295.0, 1825.0 ], "score": 0.972, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 668.0, 474.0, 668.0, 474.0, 706.0, 293.0, 706.0 ], "score": 0.998, "text": "Target Template:" } ], "page_info": { "page_no": 86, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 298, 1267, 1304, 1267, 1304, 1467, 298, 1467 ], "score": 0.961, "html": "
KeyValue
answers{'text':['cup B']}
background id Passive transport occurs when a substance passes t...
1971664873
question situationWhich cup has a higher concentration of sugar? A man put two cups， cup A and cup B， filled with e...
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703.0, 746.0, 741.0, 301.0, 741.0 ], "score": 0.999, "text": "for the following movie plot:" }, { "category_id": 15, "poly": [ 304.0, 735.0, 432.0, 735.0, 432.0, 767.0, 304.0, 767.0 ], "score": 0.996, "text": "{{plot}}" }, { "category_id": 15, "poly": [ 306.0, 320.0, 538.0, 320.0, 538.0, 347.0, 306.0, 347.0 ], "score": 0.976, "text": "{% if no_answer" }, { "category_id": 15, "poly": [ 545.0, 322.0, 574.0, 322.0, 574.0, 345.0, 545.0, 345.0 ], "score": 0.91, "text": "%}" }, { "category_id": 15, "poly": [ 302.0, 347.0, 446.0, 347.0, 446.0, 374.0, 302.0, 374.0 ], "score": 0.998, "text": "No answer" }, { "category_id": 15, "poly": [ 304.0, 373.0, 459.0, 373.0, 459.0, 406.0, 304.0, 406.0 ], "score": 0.957, "text": "{% else %}" }, { "category_id": 15, "poly": [ 305.0, 398.0, 624.0, 398.0, 624.0, 434.0, 305.0, 434.0 ], "score": 0.98, "text": "{{answers丨 choice }}" }, { "category_id": 15, "poly": [ 304.0, 425.0, 476.0, 425.0, 476.0, 461.0, 304.0, 461.0 ], "score": 0.956, "text": "{% endif %}" 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(2019). Used in training." }, { "category_id": 15, "poly": [ 304.0, 896.0, 487.0, 896.0, 487.0, 929.0, 304.0, 929.0 ], "score": 0.996, "text": "{{question}}" }, { "category_id": 15, "poly": [ 307.0, 926.0, 474.0, 926.0, 474.0, 957.0, 307.0, 957.0 ], "score": 0.974, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 1598.0, 463.0, 1598.0, 463.0, 1634.0, 295.0, 1634.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 302.0, 1874.0, 641.0, 1874.0, 641.0, 1911.0, 302.0, 1911.0 ], "score": 0.977, "text": "Hint: {{ background }}" }, { "category_id": 15, "poly": [ 294.0, 1983.0, 475.0, 1983.0, 475.0, 2022.0, 294.0, 2022.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 304.0, 1763.0, 521.0, 1763.0, 521.0, 1801.0, 304.0, 1801.0 ], "score": 0.978, "text": "{{ question }}" }, { "category_id": 15, "poly": [ 294.0, 840.0, 475.0, 840.0, 475.0, 878.0, 294.0, 878.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, 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746.0, 712.0, 776.0, 342.0, 776.0 ], "score": 0.969, "text": " answers.text | choice }}" }, { "category_id": 15, "poly": [ 304.0, 773.0, 474.0, 773.0, 474.0, 807.0, 304.0, 807.0 ], "score": 0.978, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 308.0, 1250.0, 712.0, 1250.0, 712.0, 1281.0, 308.0, 1281.0 ], "score": 0.945, "text": "{{ answers.text | choice }}" }, { "category_id": 15, "poly": [ 303.0, 1275.0, 477.0, 1275.0, 477.0, 1310.0, 303.0, 1310.0 ], "score": 0.972, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 311.0, 1836.0, 711.0, 1836.0, 711.0, 1868.0, 311.0, 1868.0 ], "score": 0.962, "text": "{{ answers.text | choice }}" }, { "category_id": 15, "poly": [ 304.0, 1863.0, 476.0, 1863.0, 476.0, 1896.0, 304.0, 1896.0 ], "score": 0.941, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 437.0, 624.0, 437.0, 624.0, 471.0, 306.0, 471.0 ], "score": 0.956, "text": "{% if answers.text %}" }, { "category_id": 15, "poly": [ 301.0, 464.0, 967.0, 464.0, 967.0, 500.0, 301.0, 500.0 ], "score": 0.977, "text": "You are given a new situation: {{situation}}" }, { "category_id": 15, "poly": [ 300.0, 518.0, 714.0, 518.0, 714.0, 557.0, 300.0, 557.0 ], "score": 0.976, "text": "and a hint : {{background}}" }, { "category_id": 15, "poly": [ 299.0, 572.0, 938.0, 572.0, 938.0, 613.0, 299.0, 613.0 ], "score": 0.971, "text": " Please answer this question : {{question}}" }, { "category_id": 15, "poly": [ 302.0, 1528.0, 1270.0, 1528.0, 1270.0, 1562.0, 302.0, 1562.0 ], "score": 0.994, "text": "Given the paragraph above， please answer correctly the following" }, { "category_id": 15, "poly": [ 300.0, 1556.0, 445.0, 1556.0, 445.0, 1589.0, 300.0, 1589.0 ], "score": 1.0, "text": "question:" }, { "category_id": 15, "poly": [ 305.0, 1610.0, 520.0, 1610.0, 520.0, 1645.0, 305.0, 1645.0 ], "score": 0.975, "text": "{{ question }}" }, { "category_id": 15, "poly": [ 302.0, 1664.0, 639.0, 1664.0, 639.0, 1701.0, 302.0, 1701.0 ], "score": 0.98, "text": "Hint: {{ background }}" }, { "category_id": 15, "poly": [ 305.0, 1445.0, 583.0, 1445.0, 583.0, 1475.0, 305.0, 1475.0 ], "score": 0.981, "text": "{% if answers.text" }, { "category_id": 15, "poly": [ 589.0, 1450.0, 620.0, 1450.0, 620.0, 1473.0, 589.0, 1473.0 ], "score": 0.986, "text": "%}" }, { "category_id": 15, "poly": [ 302.0, 1473.0, 531.0, 1473.0, 531.0, 1504.0, 302.0, 1504.0 ], "score": 0.974, "text": "{{ situation }}" }, { "category_id": 15, "poly": [ 302.0, 940.0, 621.0, 940.0, 621.0, 974.0, 302.0, 974.0 ], "score": 0.989, "text": "{% if answers.text %}" }, { "category_id": 15, "poly": [ 301.0, 967.0, 863.0, 967.0, 863.0, 1003.0, 301.0, 1003.0 ], "score": 0.991, "text": "I have a new situation: {{situation}}" }, { "category_id": 15, "poly": [ 301.0, 1024.0, 980.0, 1024.0, 980.0, 1057.0, 301.0, 1057.0 ], "score": 0.979, "text": "But I can use this background: {{background}}" }, { "category_id": 15, "poly": [ 300.0, 1079.0, 1042.0, 1079.0, 1042.0, 1115.0, 300.0, 1115.0 ], "score": 0.987, "text": "What is an answer for this question: {{question}}" }, { "category_id": 15, "poly": [ 313.0, 242.0, 711.0, 242.0, 711.0, 274.0, 313.0, 274.0 ], "score": 0.957, "text": "{{ answers.text | choice }}" }, { "category_id": 15, "poly": [ 304.0, 269.0, 477.0, 269.0, 477.0, 302.0, 304.0, 302.0 ], "score": 0.954, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 293.0, 1189.0, 474.0, 1189.0, 474.0, 1227.0, 293.0, 1227.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 295.0, 380.0, 463.0, 380.0, 463.0, 416.0, 295.0, 416.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1387.0, 463.0, 1387.0, 463.0, 1423.0, 295.0, 1423.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1776.0, 475.0, 1776.0, 475.0, 1814.0, 294.0, 1814.0 ], "score": 0.991, "text": "Target Template:" } ], "page_info": { "page_no": 90, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 298, 967, 1274, 967, 1274, 1166, 298, 1166 ], "score": 0.966, "html": "
KeyValue
id56be85543aeaaa14008c9063
titleBeyoncé
contextBeyoncé Giselle Knowles-Carter (/bijnse/ bee-Y...
questionWhen did Beyonce start becoming popular?
answers{'text':['in the late 1990s']， 'answer_start':[2...
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"poly": [ 517.0, 722.0, 526.0, 722.0, 526.0, 755.0, 517.0, 755.0 ], "score": 0.0, "text": "" }, { "category_id": 15, "poly": [ 306.0, 749.0, 1042.0, 749.0, 1042.0, 783.0, 306.0, 783.0 ], "score": 0.999, "text": "'Determine the topic of the question-answer pair." }, { "category_id": 15, "poly": [ 306.0, 773.0, 554.0, 773.0, 554.0, 811.0, 306.0, 811.0 ], "score": 0.993, "text": "'Find the topic." }, { "category_id": 15, "poly": [ 305.0, 802.0, 749.0, 802.0, 749.0, 839.0, 305.0, 839.0 ], "score": 0.987, "text": "'What is the topic from this?" }, { "category_id": 15, "poly": [ 303.0, 832.0, 376.0, 832.0, 376.0, 867.0, 303.0, 867.0 ], "score": 0.528, "text": "]%}" }, { "category_id": 15, "poly": [ 304.0, 860.0, 601.0, 860.0, 601.0, 892.0, 304.0, 892.0 ], "score": 0.967, "text": "{% if answers.text !" }, { "category_id": 15, "poly": [ 627.0, 860.0, 674.0, 860.0, 674.0, 892.0, 627.0, 892.0 ], "score": 0.784, "text": "[]" }, { "category_id": 15, "poly": [ 678.0, 863.0, 712.0, 863.0, 712.0, 890.0, 678.0, 890.0 ], "score": 0.789, "text": "%}" }, { "category_id": 15, "poly": [ 304.0, 889.0, 578.0, 889.0, 578.0, 920.0, 304.0, 920.0 ], "score": 0.914, "text": "{{ seq 丨 choice }}" }, { "category_id": 15, "poly": [ 303.0, 916.0, 1209.0, 916.0, 1209.0, 951.0, 303.0, 951.0 ], "score": 0.984, "text": "Question: {{question}}; Answer: {{answers.text[0]}}; Topic:" }, { "category_id": 15, "poly": [ 306.0, 244.0, 602.0, 244.0, 602.0, 273.0, 306.0, 273.0 ], "score": 0.988, "text": "{% if answers.text !" }, { "category_id": 15, "poly": [ 627.0, 244.0, 667.0, 244.0, 667.0, 273.0, 627.0, 273.0 ], "score": 0.95, "text": "[]" }, { "category_id": 15, "poly": [ 679.0, 245.0, 712.0, 245.0, 712.0, 271.0, 679.0, 271.0 ], "score": 0.91, "text": "%}" }, { "category_id": 15, "poly": [ 308.0, 272.0, 623.0, 272.0, 623.0, 301.0, 308.0, 301.0 ], "score": 0.985, "text": "{{answer_choices[0]}}" }, { "category_id": 15, "poly": [ 308.0, 298.0, 460.0, 298.0, 460.0, 328.0, 308.0, 328.0 ], "score": 0.957, 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KeyValue
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301.0, 1635.0 ], "score": 0.951, "text": "entity) }} Ill {% endfor %} {{ query 「 replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 302.0, 1627.0, 551.0, 1627.0, 551.0, 1665.0, 302.0, 1665.0 ], "score": 0.926, "text": "entities[-1]) }}" }, { "category_id": 15, "poly": [ 305.0, 918.0, 1323.0, 918.0, 1323.0, 954.0, 305.0, 954.0 ], "score": 0.974, "text": "{% for entity in entities[:-1] %} {{ query I replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 301.0, 947.0, 1228.0, 947.0, 1228.0, 980.0, 301.0, 980.0 ], "score": 0.951, "text": "entity) }} Ill {% endfor %} {{ query 「 replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 302.0, 977.0, 551.0, 977.0, 551.0, 1010.0, 302.0, 1010.0 ], "score": 0.94, "text": "entities[-l])}}" }, { "category_id": 15, "poly": [ 303.0, 1819.0, 504.0, 1819.0, 504.0, 1853.0, 303.0, 1853.0 ], "score": 0.987, "text": "{{ passage }}" }, { "category_id": 15, "poly": [ 305.0, 1845.0, 478.0, 1845.0, 478.0, 1883.0, 305.0, 1883.0 ], "score": 0.928, "text": "{{ query}}" }, { "category_id": 15, "poly": [ 299.0, 1871.0, 786.0, 1871.0, 786.0, 1911.0, 299.0, 1911.0 ], "score": 0.986, "text": " What could the \"{{\"@placeholder\"" }, { "category_id": 15, "poly": [ 815.0, 1871.0, 1332.0, 1871.0, 1332.0, 1911.0, 815.0, 1911.0 ], "score": 0.922, "text": "\" be?{{ entities 丨 join(\"，\") }}?" }, { "category_id": 15, "poly": [ 301.0, 565.0, 1250.0, 565.0, 1250.0, 598.0, 301.0, 598.0 ], "score": 0.979, "text": "After reading the article, write another sentence to add to it." }, { "category_id": 15, "poly": [ 301.0, 590.0, 942.0, 590.0, 942.0, 625.0, 301.0, 625.0 ], "score": 0.959, "text": "{{ passage | replace(\"@highlight\"，\"\\n- \")" }, { "category_id": 15, "poly": [ 948.0, 598.0, 978.0, 598.0, 978.0, 621.0, 948.0, 621.0 ], "score": 0.954, "text": "}}" }, { "category_id": 15, "poly": [ 302.0, 1163.0, 1391.0, 1163.0, 1391.0, 1197.0, 302.0, 1197.0 ], "score": 0.999, "text": "Please read the following news article and write another 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1203.0, 463.0, 1240.0, 295.0, 1240.0 ], "score": 0.971, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1015.0, 575.0, 1015.0, 575.0, 1055.0, 294.0, 1055.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1420.0, 475.0, 1420.0, 475.0, 1459.0, 294.0, 1459.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 296.0, 1580.0, 573.0, 1580.0, 573.0, 1616.0, 296.0, 1616.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 314.0, 575.0, 314.0, 575.0, 354.0, 294.0, 354.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 301.0, 1258.0, 506.0, 1258.0, 506.0, 1295.0, 301.0, 1295.0 ], "score": 0.959, "text": "{{ passage }}" }, { "category_id": 15, "poly": [ 306.0, 1287.0, 477.0, 1287.0, 477.0, 1321.0, 306.0, 1321.0 ], "score": 0.916, "text": "{{ query }}" }, { "category_id": 15, "poly": [ 300.0, 1310.0, 779.0, 1310.0, 779.0, 1349.0, 300.0, 1349.0 ], "score": 0.962, "text": " Here, the placeholder refers to" }, { "category_id": 15, "poly": [ 305.0, 887.0, 799.0, 887.0, 799.0, 919.0, 305.0, 919.0 ], "score": 0.911, "text": "{% if （ answers 丨 length ）> 0 %}" }, { "category_id": 15, "poly": [ 305.0, 917.0, 632.0, 917.0, 632.0, 948.0, 305.0, 948.0 ], "score": 0.964, "text": "{{ answers 丨 choice }}" }, { "category_id": 15, "poly": [ 304.0, 940.0, 476.0, 940.0, 476.0, 975.0, 304.0, 975.0 ], "score": 0.983, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 1473.0, 703.0, 1473.0, 703.0, 1514.0, 304.0, 1514.0 ], "score": 0.925, "text": "{% if（ answers 丨 length ）" }, { "category_id": 15, "poly": [ 731.0, 1473.0, 1133.0, 1473.0, 1133.0, 1514.0, 731.0, 1514.0 ], "score": 0.949, "text": "0 %}{{ answers 丨 choice }}" }, { "category_id": 15, "poly": [ 304.0, 1503.0, 477.0, 1503.0, 477.0, 1540.0, 304.0, 1540.0 ], "score": 0.918, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 302.0, 557.0, 505.0, 557.0, 505.0, 592.0, 302.0, 592.0 ], "score": 0.98, "text": "{{ passage }}" }, { "category_id": 15, "poly": [ 303.0, 585.0, 477.0, 585.0, 477.0, 623.0, 303.0, 623.0 ], "score": 0.914, "text": "{{ query }}" }, { "category_id": 15, "poly": [ 305.0, 1634.0, 729.0, 1634.0, 729.0, 1671.0, 305.0, 1671.0 ], "score": 0.911, "text": "{{ entities 丨 join(\"lll\") }}" }, { "category_id": 15, "poly": [ 305.0, 370.0, 729.0, 370.0, 729.0, 408.0, 305.0, 408.0 ], "score": 0.895, "text": "{{ entities 丨 join(\"Ill\") }}" }, { "category_id": 15, "poly": [ 307.0, 1075.0, 698.0, 1075.0, 698.0, 1107.0, 307.0, 1107.0 ], "score": 0.917, "text": "{{entities | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 304.0, 1820.0, 849.0, 1820.0, 849.0, 1856.0, 304.0, 1856.0 ], "score": 0.978, "text": "{{ passage.split(\"@highlight\") [0] }}" }, { "category_id": 15, "poly": [ 301.0, 1875.0, 477.0, 1875.0, 477.0, 1912.0, 301.0, 1912.0 ], "score": 1.0, "text": "Highlights:" }, { "category_id": 15, "poly": [ 313.0, 1926.0, 1121.0, 1926.0, 1121.0, 1973.0, 313.0, 1973.0 ], "score": 0.958, "text": "{{ passage.split(\"@highlight\")[1:] | join(\"\\n- \"） }}" }, { "category_id": 15, "poly": [ 300.0, 1987.0, 862.0, 1987.0, 862.0, 2022.0, 300.0, 2022.0 ], "score": 0.99, "text": " Please write an additional highlight." }, { "category_id": 15, "poly": [ 304.0, 240.0, 1299.0, 240.0, 1299.0, 279.0, 304.0, 279.0 ], "score": 0.94, "text": "{% if （ answers 丨 length ）> O %}{{ answers 丨 choice }}{% endif %}" }, { "category_id": 15, "poly": [ 294.0, 314.0, 575.0, 314.0, 575.0, 354.0, 294.0, 354.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 296.0, 1580.0, 573.0, 1580.0, 573.0, 1616.0, 296.0, 1616.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 299.0, 638.0, 1344.0, 638.0, 1344.0, 673.0, 299.0, 673.0 ], "score": 0.978, "text": " I am trying to decide what \"{{\"@placeholder\"}}\" means in the previous" }, { "category_id": 15, "poly": [ 301.0, 670.0, 387.0, 670.0, 387.0, 702.0, 301.0, 702.0 ], "score": 1.0, "text": "text." }, { "category_id": 15, "poly": [ 302.0, 699.0, 835.0, 699.0, 835.0, 731.0, 302.0, 731.0 ], "score": 0.999, "text": "Help by choosing an option between:" }, { "category_id": 15, "poly": [ 308.0, 731.0, 329.0, 731.0, 329.0, 750.0, 308.0, 750.0 ], "score": 0.582, "text": "1" }, { "category_id": 15, "poly": [ 330.0, 724.0, 693.0, 724.0, 693.0, 756.0, 330.0, 756.0 ], "score": 0.955, "text": "{{ entities | join(\"\\n-" }, { "category_id": 15, "poly": [ 697.0, 730.0, 713.0, 730.0, 713.0, 745.0, 697.0, 745.0 ], "score": 0.536, "text": "\"" }, { "category_id": 15, "poly": [ 294.0, 1420.0, 475.0, 1420.0, 475.0, 1459.0, 294.0, 1459.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1015.0, 575.0, 1015.0, 575.0, 1055.0, 294.0, 1055.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1203.0, 463.0, 1203.0, 463.0, 1240.0, 295.0, 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"text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 945.0, 474.0, 945.0, 474.0, 984.0, 293.0, 984.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 422.0, 573.0, 422.0, 573.0, 460.0, 295.0, 460.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1133.0, 575.0, 1133.0, 575.0, 1173.0, 294.0, 1173.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1982.0, 463.0, 1982.0, 463.0, 2018.0, 295.0, 2018.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 1002.0, 799.0, 1002.0, 799.0, 1034.0, 304.0, 1034.0 ], "score": 0.91, "text": "{% if （ answers 丨 length ）> 0 %}" }, { "category_id": 15, "poly": [ 305.0, 1033.0, 603.0, 1033.0, 603.0, 1064.0, 305.0, 1064.0 ], "score": 0.954, "text": "{{ answers 丨 choice" }, { "category_id": 15, "poly": [ 304.0, 1057.0, 475.0, 1057.0, 475.0, 1092.0, 304.0, 1092.0 ], "score": 0.983, "text": "{% endif %}" }, { "category_id": 16, "poly": [ 610.0, 1040.0, 625.0, 1040.0, 625.0, 1055.0, 610.0, 1055.0 ], "score": 0.254, "text": "？" }, { "category_id": 15, "poly": [ 304.0, 1659.0, 800.0, 1659.0, 800.0, 1693.0, 304.0, 1693.0 ], "score": 0.925, "text": "{% if（answers 丨 length ）> 0 %}" }, { "category_id": 15, "poly": [ 304.0, 1690.0, 603.0, 1690.0, 603.0, 1719.0, 304.0, 1719.0 ], "score": 0.959, "text": "{{ answers 丨 choice" }, { "category_id": 15, "poly": [ 604.0, 1694.0, 632.0, 1694.0, 632.0, 1717.0, 604.0, 1717.0 ], "score": 0.822, "text": "}}" }, { "category_id": 15, "poly": [ 304.0, 1714.0, 476.0, 1714.0, 476.0, 1750.0, 304.0, 1750.0 ], "score": 0.945, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 479.0, 1353.0, 479.0, 1353.0, 515.0, 305.0, 515.0 ], "score": 0.982, "text": "{% for entity in entities[:-1] %} - {{ query | replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 302.0, 508.0, 1262.0, 508.0, 1262.0, 544.0, 302.0, 544.0 ], "score": 0.934, "text": "entity) }} Ill {% endfor %} - {{ query 「 replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 302.0, 538.0, 550.0, 538.0, 550.0, 571.0, 302.0, 571.0 ], "score": 0.924, "text": "entities[-l])}}" }, { "category_id": 15, "poly": [ 304.0, 808.0, 504.0, 808.0, 504.0, 843.0, 304.0, 843.0 ], "score": 0.959, "text": "{{ passage }}" }, { "category_id": 15, "poly": [ 303.0, 835.0, 477.0, 835.0, 477.0, 874.0, 303.0, 874.0 ], "score": 0.945, "text": "{{ query}}" }, { "category_id": 15, "poly": [ 302.0, 1382.0, 505.0, 1382.0, 505.0, 1417.0, 302.0, 1417.0 ], "score": 0.98, "text": "{{ passage }}" }, { "category_id": 15, "poly": [ 303.0, 1410.0, 477.0, 1410.0, 477.0, 1448.0, 303.0, 1448.0 ], "score": 0.914, "text": "{{ query }}" }, { "category_id": 15, "poly": [ 300.0, 1465.0, 1030.0, 1465.0, 1030.0, 1500.0, 300.0, 1500.0 ], "score": 0.98, "text": "Pick one option,\"{{\"@placeholder\"}}\" refers to:" }, { "category_id": 15, "poly": [ 332.0, 1493.0, 833.0, 1493.0, 833.0, 1528.0, 332.0, 1528.0 ], "score": 0.952, "text": "{{answer_choices l join(\"\\n- \") }}" }, { "category_id": 15, "poly": [ 306.0, 315.0, 1335.0, 315.0, 1335.0, 353.0, 306.0, 353.0 ], "score": 0.944, "text": "{% if （ answers 丨 length ）> O %}- {{ query 丨 replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 302.0, 350.0, 433.0, 350.0, 433.0, 377.0, 302.0, 377.0 ], "score": 1.0, "text": "answers" }, { "category_id": 15, "poly": [ 446.0, 345.0, 790.0, 345.0, 790.0, 380.0, 446.0, 380.0 ], "score": 0.903, "text": " choice)}} {% endif %}" }, { "category_id": 15, "poly": [ 307.0, 1195.0, 698.0, 1195.0, 698.0, 1227.0, 307.0, 1227.0 ], "score": 0.917, "text": "{{entities | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 307.0, 1852.0, 697.0, 1852.0, 697.0, 1884.0, 307.0, 1884.0 ], "score": 0.928, "text": "{{entities | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 304.0, 726.0, 1134.0, 726.0, 1134.0, 757.0, 304.0, 757.0 ], "score": 0.995, "text": "Exercise: Extract from the text the correct entity that" }, { "category_id": 15, "poly": [ 310.0, 752.0, 865.0, 752.0, 865.0, 790.0, 310.0, 790.0 ], "score": 0.996, "text": "\"{{\"@placeholder\"}}\" is referring to." }, { "category_id": 15, "poly": [ 295.0, 1982.0, 463.0, 1982.0, 463.0, 2018.0, 295.0, 2018.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1133.0, 575.0, 1133.0, 575.0, 1173.0, 294.0, 1173.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 422.0, 573.0, 422.0, 573.0, 460.0, 295.0, 460.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 293.0, 945.0, 474.0, 945.0, 474.0, 984.0, 293.0, 984.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 296.0, 1793.0, 573.0, 1793.0, 573.0, 1829.0, 296.0, 1829.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 668.0, 463.0, 668.0, 463.0, 704.0, 295.0, 704.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1325.0, 463.0, 1325.0, 463.0, 1361.0, 295.0, 1361.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1602.0, 474.0, 1602.0, 474.0, 1640.0, 293.0, 1640.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 260.0, 474.0, 260.0, 474.0, 298.0, 293.0, 298.0 ], "score": 0.998, "text": "Target Template:" } ], "page_info": { "page_no": 101, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 305, 1370, 1353, 1370, 1353, 1457, 305, 1457 ], "score": 0.954 }, { "category_id": 1, "poly": [ 306, 1620, 1351, 1620, 1351, 1708, 306, 1708 ], "score": 0.939 }, { "category_id": 1, "poly": [ 302, 583, 1370, 583, 1370, 671, 302, 671 ], "score": 0.922 }, { "category_id": 1, "poly": [ 305, 414, 1347, 414, 1347, 474, 305, 474 ], "score": 0.909 }, { "category_id": 1, "poly": [ 301, 1201, 1340, 1201, 1340, 1260, 301, 1260 ], "score": 0.878 }, { "category_id": 2, "poly": [ 829, 2088, 871, 2088, 871, 2114, 829, 2114 ], "score": 0.869 }, { "category_id": 1, "poly": [ 296, 774, 931, 774, 931, 834, 296, 834 ], "score": 0.861 }, { "category_id": 2, "poly": [ 298, 74, 816, 74, 816, 106, 298, 106 ], "score": 0.86 }, { "category_id": 1, "poly": [ 305, 242, 980, 242, 980, 275, 305, 275 ], "score": 0.858 }, { "category_id": 1, "poly": [ 310, 1988, 726, 1988, 726, 2020, 310, 2020 ], "score": 0.853 }, { "category_id": 0, "poly": [ 298, 355, 468, 355, 468, 385, 298, 385 ], "score": 0.818 }, { "category_id": 0, "poly": [ 298, 1142, 468, 1142, 468, 1173, 298, 1173 ], "score": 0.796 }, { "category_id": 0, "poly": [ 298, 1561, 459, 1561, 459, 1591, 298, 1591 ], "score": 0.773 }, { "category_id": 1, "poly": [ 306, 976, 470, 976, 470, 1004, 306, 1004 ], "score": 0.76 }, { "category_id": 0, "poly": [ 298, 1789, 468, 1789, 468, 1818, 298, 1818 ], "score": 0.729 }, { "category_id": 1, "poly": [ 311, 919, 845, 919, 845, 951, 311, 951 ], "score": 0.694 }, { "category_id": 1, "poly": [ 305, 1847, 1307, 1847, 1307, 1881, 305, 1881 ], "score": 0.668 }, { "category_id": 0, "poly": [ 299, 1929, 570, 1929, 570, 1959, 299, 1959 ], "score": 0.531 }, { "category_id": 1, "poly": [ 300, 523, 570, 523, 570, 554, 300, 554 ], "score": 0.531 }, { "category_id": 1, "poly": [ 304, 866, 426, 866, 426, 893, 304, 893 ], "score": 0.52 }, { "category_id": 0, "poly": [ 299, 1311, 570, 1311, 570, 1341, 299, 1341 ], "score": 0.489 }, { "category_id": 1, "poly": [ 299, 1311, 570, 1311, 570, 1341, 299, 1341 ], "score": 0.469 }, { "category_id": 1, "poly": [ 299, 1929, 570, 1929, 570, 1959, 299, 1959 ], "score": 0.433 }, { "category_id": 0, "poly": [ 300, 523, 570, 523, 570, 554, 300, 554 ], "score": 0.432 }, { "category_id": 1, "poly": [ 307, 1031, 1115, 1031, 1115, 1063, 307, 1063 ], "score": 0.409 }, { "category_id": 1, "poly": [ 298, 1789, 468, 1789, 468, 1818, 298, 1818 ], "score": 0.206 }, { "category_id": 1, "poly": [ 298, 1142, 468, 1142, 468, 1173, 298, 1173 ], "score": 0.12 }, { "category_id": 1, "poly": [ 298, 1561, 459, 1561, 459, 1591, 298, 1591 ], "score": 0.119 }, { "category_id": 1, "poly": [ 298, 355, 468, 355, 468, 385, 298, 385 ], "score": 0.096 }, { "category_id": 15, "poly": [ 823.0, 2085.0, 877.0, 2085.0, 877.0, 2124.0, 823.0, 2124.0 ], "score": 1.0, "text": "103" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 352.0, 474.0, 352.0, 474.0, 390.0, 293.0, 390.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1140.0, 474.0, 1140.0, 474.0, 1176.0, 294.0, 1176.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1559.0, 463.0, 1559.0, 463.0, 1595.0, 294.0, 1595.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1785.0, 474.0, 1785.0, 474.0, 1824.0, 293.0, 1824.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1924.0, 575.0, 1924.0, 575.0, 1964.0, 294.0, 1964.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1306.0, 575.0, 1306.0, 575.0, 1346.0, 294.0, 1346.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 520.0, 573.0, 520.0, 573.0, 558.0, 295.0, 558.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 304.0, 1367.0, 1348.0, 1367.0, 1348.0, 1403.0, 304.0, 1403.0 ], "score": 0.972, "text": "{% for entity in entities[:-1] %} - {{ query 丨 replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 303.0, 1397.0, 1257.0, 1397.0, 1257.0, 1430.0, 303.0, 1430.0 ], "score": 0.938, "text": "entity) }} Ill {% endfor %} - {{ query 「 replace(\"@placeholder\"," }, { "category_id": 15, "poly": [ 303.0, 1428.0, 549.0, 1428.0, 549.0, 1457.0, 303.0, 1457.0 ], "score": 0.934, "text": "entities[-1]）}}" }, { "category_id": 15, "poly": [ 304.0, 1619.0, 505.0, 1619.0, 505.0, 1653.0, 304.0, 1653.0 ], "score": 0.966, "text": "{{ passage }}" }, { "category_id": 15, "poly": [ 307.0, 1648.0, 478.0, 1648.0, 478.0, 1682.0, 307.0, 1682.0 ], "score": 0.914, "text": "{{ query }}" }, { "category_id": 15, "poly": [ 303.0, 1672.0, 1332.0, 1672.0, 1332.0, 1709.0, 303.0, 1709.0 ], "score": 0.992, "text": "Can you figure out what does the \"{{\"@placeholder\"}}\" mean? 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KeyValue
answers{'answer_start':[250]，'text':['Catherine']}
contextThe earthquake swarm was noted on October 12，2007...
idba3f052c7a557909526b59713430403dd134e01d
questionWhat is the first name of the person who doubted i...
title2007{2008 Nazko earthquakes 1
urlhttps://en.wikipedia.org/wiki/2007%E2%80%932008_Na...
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KeyValue
abstractive_explanationwebmath is designed to help you solve
answer choicesmath problem ['park'，'coloring book'，'garden center'，'math p...
extractive_explanation"there are 10 apples on an apple tree. three fall ...
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"Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1158.0, 475.0, 1158.0, 475.0, 1197.0, 294.0, 1197.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1596.0, 474.0, 1596.0, 474.0, 1635.0, 294.0, 1635.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 506.0, 475.0, 506.0, 475.0, 545.0, 294.0, 545.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 640.0, 574.0, 640.0, 574.0, 675.0, 295.0, 675.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 299.0, 1785.0, 690.0, 1785.0, 690.0, 1821.0, 299.0, 1821.0 ], "score": 0.972, "text": "AIIIBIIICIIIDIIIE" }, { "category_id": 15, "poly": [ 303.0, 563.0, 486.0, 563.0, 486.0, 596.0, 303.0, 596.0 ], "score": 0.981, "text": "{{ answer }}" }, { "category_id": 15, "poly": [ 295.0, 1729.0, 574.0, 1729.0, 574.0, 1764.0, 295.0, 1764.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { 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line with common sense to answer the question." }, { "category_id": 15, "poly": [ 301.0, 342.0, 686.0, 342.0, 686.0, 380.0, 301.0, 380.0 ], "score": 0.983, "text": "Questions: {{ question }}" }, { "category_id": 15, "poly": [ 302.0, 372.0, 433.0, 372.0, 433.0, 408.0, 302.0, 408.0 ], "score": 1.0, "text": "Options:" }, { "category_id": 15, "poly": [ 330.0, 396.0, 864.0, 396.0, 864.0, 436.0, 330.0, 436.0 ], "score": 0.938, "text": "{{ answer_choices 丨 join(\"\\n- \") }}" }, { "category_id": 15, "poly": [ 294.0, 824.0, 933.0, 824.0, 933.0, 862.0, 294.0, 862.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 860.0, 462.0, 860.0, 462.0, 892.0, 295.0, 892.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1996.0, 871.0, 1996.0, 871.0, 2043.0, 293.0, 2043.0 ], "score": 0.981, "text": "Dataset from Huang et al. (2019). Used in training." }, { "category_id": 15, "poly": [ 304.0, 1653.0, 954.0, 1653.0, 954.0, 1688.0, 304.0, 1688.0 ], "score": 0.988, "text": "{{ answer_choices[choices.index(answer)] }}" }, { "category_id": 15, "poly": [ 305.0, 693.0, 713.0, 693.0, 713.0, 732.0, 305.0, 732.0 ], "score": 0.933, "text": "{{ choices 丨 join(\"lll\") }}" }, { "category_id": 15, "poly": [ 301.0, 911.0, 803.0, 911.0, 803.0, 948.0, 301.0, 948.0 ], "score": 0.975, "text": "Here's a question: {{ question }}" }, { "category_id": 15, "poly": [ 305.0, 1214.0, 743.0, 1214.0, 743.0, 1250.0, 305.0, 1250.0 ], "score": 0.99, "text": "{{ abstractive_explanation }}" }, { "category_id": 15, "poly": [ 305.0, 561.0, 491.0, 561.0, 491.0, 599.0, 305.0, 599.0 ], "score": 0.978, "text": "{{ answer }}" }, { "category_id": 15, "poly": [ 295.0, 1729.0, 574.0, 1729.0, 574.0, 1764.0, 295.0, 1764.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 640.0, 574.0, 640.0, 574.0, 675.0, 295.0, 675.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 506.0, 475.0, 506.0, 475.0, 545.0, 294.0, 545.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 301.0, 969.0, 954.0, 969.0, 954.0, 1002.0, 301.0, 1002.0 ], "score": 0.989, "text": " Here are possible answers to this question:" }, { "category_id": 15, "poly": [ 299.0, 993.0, 761.0, 993.0, 761.0, 1031.0, 299.0, 1031.0 ], "score": 0.92, "text": "- {{ choices 丨 join(\"\\n- \") }}" }, { "category_id": 15, "poly": [ 299.0, 1050.0, 1163.0, 1050.0, 1163.0, 1086.0, 299.0, 1086.0 ], "score": 0.984, "text": "I believe the correct choice is \"{{answer}}\"， here's why:" }, { "category_id": 15, "poly": [ 294.0, 1596.0, 474.0, 1596.0, 474.0, 1635.0, 294.0, 1635.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1158.0, 475.0, 1158.0, 475.0, 1197.0, 294.0, 1197.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 299.0, 1785.0, 690.0, 1785.0, 690.0, 1821.0, 299.0, 1821.0 ], "score": 0.979, "text": "AIIIBIIICIIIDIIIE" }, { "category_id": 15, "poly": [ 295.0, 1322.0, 463.0, 1322.0, 463.0, 1359.0, 295.0, 1359.0 ], "score": 0.972, "text": "Input Template:" } ], "page_info": { "page_no": 109, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 296, 317, 1273, 317, 1273, 602, 296, 602 ], "score": 0.978, "html": "
Key Value
answerONone of the above choices .
answer1 This person likes music and likes to see the show ...
answer2This person only likes Good Old War and Person L ,..
answer3Other Bands is not on tour and this person can not...
contextGood Old War and person L :I saw both of these ba...
id3Q9SPIIRWJKVQ8244310E8TUS6YWAC##34V1S5K3GTZMDUBNBI...
label1
questionIn the future ，will this person go to see other b...
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KeyValue
answerKeyF
choices{'label'：［'A'，'B'，'C'，'D'，'E'，'F'，'G'，'H']...
combinedfactBeads of water can be formed by clouds.
fact1beads of water are formed by water vapor condensin...
fact2Clouds are made of water vapor.
formatted_question idWhat type of water formation is formed by clouds? ：： 3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K
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294.0, 432.0, 474.0, 432.0, 474.0, 468.0, 294.0, 468.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 894.0, 473.0, 894.0, 473.0, 934.0, 294.0, 934.0 ], "score": 0.973, "text": "Data Example" }, { "category_id": 15, "poly": [ 293.0, 1668.0, 474.0, 1668.0, 474.0, 1706.0, 293.0, 1706.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 300.0, 1852.0, 465.0, 1852.0, 465.0, 1890.0, 300.0, 1890.0 ], "score": 0.89, "text": "Yes III No" }, { "category_id": 15, "poly": [ 295.0, 1985.0, 464.0, 1985.0, 464.0, 2021.0, 295.0, 2021.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1797.0, 575.0, 1797.0, 575.0, 1837.0, 294.0, 1837.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 563.0, 574.0, 563.0, 574.0, 598.0, 295.0, 598.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 302.0, 1478.0, 1074.0, 1478.0, 1074.0, 1513.0, 302.0, 1513.0 ], "score": 0.983, "text": "If I tell you that {{combinedfact[0]lcapitalize}}{{" }, { "category_id": 15, "poly": [ 299.0, 1503.0, 1196.0, 1503.0, 1196.0, 1544.0, 299.0, 1544.0 ], "score": 0.944, "text": "combinedfact[1:]ltrim('.'） }}， and ask you the question \"{{" }, { "category_id": 15, "poly": [ 300.0, 1532.0, 1301.0, 1532.0, 1301.0, 1570.0, 300.0, 1570.0 ], "score": 0.97, "text": "question[O]llower }}{{ question[1:] }}\", is the correct answer \"{{" }, { "category_id": 15, "poly": [ 299.0, 1560.0, 1259.0, 1560.0, 1259.0, 1598.0, 299.0, 1598.0 ], "score": 0.954, "text": "choices.text[0][0]|lower}}{{ choices.text[0][1:]|trim('.'） }}\"?" }, { "category_id": 15, "poly": [ 294.0, 1392.0, 933.0, 1392.0, 933.0, 1429.0, 294.0, 1429.0 ], "score": 0.98, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1425.0, 462.0, 1425.0, 462.0, 1457.0, 296.0, 1457.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 301.0, 296.0, 852.0, 296.0, 852.0, 329.0, 301.0, 329.0 ], "score": 1.0, "text": "Pick the right answer from the list:" }, { "category_id": 15, "poly": [ 307.0, 324.0, 864.0, 324.0, 864.0, 360.0, 307.0, 360.0 ], "score": 0.965, "text": "- {{ answer_choices | join(\"\\n- \") }}" }, { "category_id": 15, "poly": [ 294.0, 826.0, 854.0, 826.0, 854.0, 875.0, 294.0, 875.0 ], "score": 0.973, "text": "Dataset from Khot et al. (2020). Used in training." }, { "category_id": 15, "poly": [ 305.0, 616.0, 759.0, 616.0, 759.0, 654.0, 305.0, 654.0 ], "score": 0.926, "text": "{{choices.text | join(\"Il|\")}}" }, { "category_id": 15, "poly": [ 307.0, 487.0, 1148.0, 487.0, 1148.0, 522.0, 307.0, 522.0 ], "score": 0.991, "text": "{{answer_choices[{\"A\":0,\"B\":1,\"C\":2,\"D\":3}[answerKey]]}}" }, { "category_id": 15, "poly": [ 295.0, 563.0, 574.0, 563.0, 574.0, 598.0, 295.0, 598.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 306.0, 243.0, 563.0, 243.0, 563.0, 275.0, 306.0, 275.0 ], "score": 0.999, "text": "{{question_stem}}" }, { "category_id": 15, "poly": [ 294.0, 1797.0, 575.0, 1797.0, 575.0, 1837.0, 294.0, 1837.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 303.0, 1722.0, 539.0, 1722.0, 539.0, 1761.0, 303.0, 1761.0 ], "score": 0.998, "text": "{% if answerKey" }, { "category_id": 15, "poly": [ 584.0, 1722.0, 1329.0, 1722.0, 1329.0, 1761.0, 584.0, 1761.0 ], "score": 0.969, "text": "choices.label[0] %} Yes {% else %} No {% endif %}" }, { "category_id": 15, "poly": [ 295.0, 1985.0, 464.0, 1985.0, 464.0, 2021.0, 295.0, 2021.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1668.0, 474.0, 1668.0, 474.0, 1706.0, 293.0, 1706.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 432.0, 474.0, 432.0, 474.0, 468.0, 294.0, 468.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 300.0, 1852.0, 465.0, 1852.0, 465.0, 1890.0, 300.0, 1890.0 ], "score": 0.89, "text": "Yes III No" } ], "page_info": { "page_no": 119, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 303, 988, 1012, 988, 1012, 1044, 303, 1044 ], "score": 0.928 }, { "category_id": 1, "poly": [ 306, 243, 1390, 243, 1390, 355, 306, 355 ], "score": 0.913 }, { "category_id": 1, "poly": [ 305, 1647, 1379, 1647, 1379, 1731, 305, 1731 ], "score": 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conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 1809.0, 474.0, 1809.0, 474.0, 1848.0, 293.0, 1848.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1121.0, 474.0, 1121.0, 474.0, 1159.0, 293.0, 1159.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 461.0, 474.0, 461.0, 474.0, 499.0, 293.0, 499.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1476.0, 463.0, 1476.0, 463.0, 1512.0, 294.0, 1512.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 623.0, 575.0, 623.0, 575.0, 663.0, 294.0, 663.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 816.0, 463.0, 816.0, 463.0, 852.0, 294.0, 852.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1283.0, 575.0, 1283.0, 575.0, 1323.0, 294.0, 1323.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 301.0, 984.0, 1019.0, 984.0, 1019.0, 1021.0, 301.0, 1021.0 ], "score": 0.973, "text": "Given the two facts above， {{ question[0]llower" }, { "category_id": 15, "poly": [ 301.0, 1008.0, 766.0, 1008.0, 766.0, 1046.0, 301.0, 1046.0 ], "score": 0.903, "text": "}}{{question[1:]Itrim('?')j}?" }, { "category_id": 15, "poly": [ 304.0, 242.0, 1211.0, 242.0, 1211.0, 275.0, 304.0, 275.0 ], "score": 0.932, "text": "{{ factl[0]lcapitalize }}{{ fact1[1:]ltrim|trim('.') }}， and" }, { "category_id": 15, "poly": [ 307.0, 267.0, 1343.0, 267.0, 1343.0, 304.0, 307.0, 304.0 ], "score": 0.952, "text": "{{fact2[0]ilower }}{{ fact2[1:]Itrimitrim('.'） }}. Given these facts," }, { "category_id": 15, "poly": [ 1354.0, 273.0, 1388.0, 273.0, 1388.0, 302.0, 1354.0, 302.0 ], "score": 0.998, "text": "{{" }, { "category_id": 15, "poly": [ 301.0, 294.0, 1315.0, 294.0, 1315.0, 333.0, 301.0, 333.0 ], "score": 0.955, "text": "question[0]llower }}{{question[i:]ltrim('?'） }} among the following" }, { "category_id": 15, "poly": [ 301.0, 326.0, 431.0, 326.0, 431.0, 357.0, 301.0, 357.0 ], "score": 1.0, "text": "options:" }, { "category_id": 15, "poly": [ 303.0, 1645.0, 1230.0, 1645.0, 1230.0, 1677.0, 303.0, 1677.0 ], "score": 0.947, "text": "Now knowing that {{ factl[0]llower }}{{ fact1[1:]ltrim|trim('." }, { "category_id": 15, "poly": [ 1277.0, 1652.0, 1310.0, 1652.0, 1310.0, 1674.0, 1277.0, 1674.0 ], "score": 0.996, "text": "}}" }, { "category_id": 15, "poly": [ 1317.0, 1649.0, 1375.0, 1649.0, 1375.0, 1672.0, 1317.0, 1672.0 ], "score": 1.0, "text": "and" }, { "category_id": 15, "poly": [ 304.0, 1673.0, 1301.0, 1673.0, 1301.0, 1705.0, 304.0, 1705.0 ], "score": 0.953, "text": "{{fact2[0]llower }}{{ fact2[1:]ltrim|trim('.') }}， choose the best" }, { "category_id": 15, "poly": [ 304.0, 1705.0, 416.0, 1705.0, 416.0, 1732.0, 304.0, 1732.0 ], "score": 1.0, "text": "answer." }, { "category_id": 15, "poly": [ 306.0, 519.0, 1004.0, 519.0, 1004.0, 554.0, 306.0, 554.0 ], "score": 0.983, "text": "{% for choice in choices.label %} {% if choice" }, { "category_id": 15, "poly": [ 1047.0, 519.0, 1266.0, 519.0, 1266.0, 554.0, 1047.0, 554.0 ], "score": 0.974, "text": "answerKey %}{{" }, { "category_id": 15, "poly": [ 303.0, 547.0, 1147.0, 547.0, 1147.0, 581.0, 303.0, 581.0 ], "score": 0.984, "text": "answer_choices[loop.index - 1] }}{% endif %}{% endfor %}" }, { "category_id": 15, "poly": [ 306.0, 1867.0, 1004.0, 1867.0, 1004.0, 1901.0, 306.0, 1901.0 ], "score": 0.991, "text": "{% for choice in choices.label %} {% if choice" }, { "category_id": 15, "poly": [ 1048.0, 1867.0, 1268.0, 1867.0, 1268.0, 1901.0, 1048.0, 1901.0 ], "score": 0.991, "text": "answerKey %}{{" }, { "category_id": 15, "poly": [ 303.0, 1895.0, 1146.0, 1895.0, 1146.0, 1926.0, 303.0, 1926.0 ], "score": 0.982, "text": "answer_choices[loop.index - 1] }}{% endif %}{% endfor %}" }, { "category_id": 15, "poly": [ 306.0, 1179.0, 1005.0, 1179.0, 1005.0, 1214.0, 306.0, 1214.0 ], "score": 0.986, "text": "{% for choice in choices.label %} {% if choice" }, { "category_id": 15, "poly": [ 1048.0, 1179.0, 1266.0, 1179.0, 1266.0, 1214.0, 1048.0, 1214.0 ], "score": 0.985, "text": "answerKey %}{{" }, { "category_id": 15, "poly": [ 303.0, 1207.0, 1147.0, 1207.0, 1147.0, 1241.0, 303.0, 1241.0 ], "score": 0.989, "text": "answer_choices[loop.index - 1] }}{% endif %}{% endfor %}" }, { "category_id": 15, "poly": [ 299.0, 925.0, 1252.0, 925.0, 1252.0, 965.0, 299.0, 965.0 ], "score": 0.969, "text": "Fact 2: {{fact2[0]lcapitalize }}{{ fact2[1:]Itrim|trim('.') }}." }, { "category_id": 15, "poly": [ 305.0, 684.0, 759.0, 684.0, 759.0, 719.0, 305.0, 719.0 ], "score": 0.935, "text": "{{choices.text | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 305.0, 1344.0, 759.0, 1344.0, 759.0, 1379.0, 305.0, 1379.0 ], "score": 0.935, "text": "{{choices.text | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 295.0, 1971.0, 574.0, 1971.0, 574.0, 2009.0, 295.0, 2009.0 ], "score": 0.999, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 318.0, 351.0, 856.0, 351.0, 856.0, 386.0, 318.0, 386.0 ], "score": 0.934, "text": "· {{answer_choices | join(\"\\n - \") }}" }, { "category_id": 15, "poly": [ 298.0, 872.0, 1266.0, 872.0, 1266.0, 910.0, 298.0, 910.0 ], "score": 0.961, "text": " Fact 1: {{ fact1[0]lcapitalize }}{{ fact1[1:]Itrim|trim('.') }}." }, { "category_id": 15, "poly": [ 299.0, 1530.0, 1364.0, 1530.0, 1364.0, 1570.0, 299.0, 1570.0 ], "score": 0.989, "text": " You are presented with the question \"{{ question }}\" and the following" }, { "category_id": 15, "poly": [ 302.0, 1563.0, 535.0, 1563.0, 535.0, 1595.0, 302.0, 1595.0 ], "score": 1.0, "text": 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475.0, 2021.0, 294.0, 2021.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 302.0, 1792.0, 1389.0, 1792.0, 1389.0, 1826.0, 302.0, 1826.0 ], "score": 0.987, "text": "Do you think the right answer to the question \"{{ question[0]llower }}{{" }, { "category_id": 15, "poly": [ 299.0, 1815.0, 1093.0, 1815.0, 1093.0, 1856.0, 299.0, 1856.0 ], "score": 0.939, "text": "question[1:] }}\" is \"{{ choices.text[1][0]llower}}{{" }, { "category_id": 15, "poly": [ 302.0, 1843.0, 988.0, 1843.0, 988.0, 1885.0, 302.0, 1885.0 ], "score": 0.923, "text": "choices.text[1][1:]ltrim('.） }]\", given that" }, { "category_id": 15, "poly": [ 321.0, 1875.0, 1194.0, 1875.0, 1194.0, 1909.0, 321.0, 1909.0 ], "score": 0.96, "text": "{{combinedfact[0]ilower}}{{ combinedfact[1:]ltrim('.'） }}?" }, { "category_id": 15, "poly": [ 294.0, 1049.0, 933.0, 1049.0, 933.0, 1087.0, 294.0, 1087.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 296.0, 1085.0, 462.0, 1085.0, 462.0, 1117.0, 296.0, 1117.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 299.0, 1135.0, 1269.0, 1135.0, 1269.0, 1173.0, 299.0, 1173.0 ], "score": 0.965, "text": " If {{ combinedfact[0]llower }}{{ combinedfact[1:]ltrim|trim('.')" }, { "category_id": 15, "poly": [ 302.0, 1164.0, 1225.0, 1164.0, 1225.0, 1199.0, 302.0, 1199.0 ], "score": 0.934, "text": "then {{ question[0]|lower }}{{question[1:]Itrimltrim('?') }}?" }, { "category_id": 15, "poly": [ 307.0, 1415.0, 1004.0, 1415.0, 1004.0, 1446.0, 307.0, 1446.0 ], "score": 0.986, "text": "{% for choice in choices.label %} {% if choice" }, { "category_id": 15, "poly": [ 1048.0, 1415.0, 1199.0, 1415.0, 1199.0, 1446.0, 1048.0, 1446.0 ], "score": 0.997, "text": "answerKey" }, { "category_id": 15, "poly": [ 1200.0, 1417.0, 1266.0, 1417.0, 1266.0, 1445.0, 1200.0, 1445.0 ], "score": 0.974, "text": "%}{{" }, { "category_id": 15, "poly": [ 302.0, 1440.0, 1148.0, 1440.0, 1148.0, 1473.0, 302.0, 1473.0 ], "score": 0.978, "text": "answer_choices[loop.index - 1] }}{% endif %}{% endfor %}" }, { "category_id": 15, "poly": [ 301.0, 484.0, 1345.0, 484.0, 1345.0, 517.0, 301.0, 517.0 ], "score": 0.98, "text": "But you don't know the answer, so you turn to your teacher to ask for" }, { "category_id": 15, "poly": [ 299.0, 510.0, 1386.0, 510.0, 1386.0, 547.0, 299.0, 547.0 ], "score": 0.952, "text": "hints. He says that \"{{ factl[0]|lower }}{{ factl[1:]Itrim|trim('.') }}\"" }, { "category_id": 15, "poly": [ 297.0, 538.0, 383.0, 538.0, 383.0, 575.0, 297.0, 575.0 ], "score": 0.941, "text": "and \"" }, { "category_id": 15, "poly": [ 412.0, 538.0, 1147.0, 538.0, 1147.0, 575.0, 412.0, 575.0 ], "score": 0.951, "text": "fact2[0]llower }}{{ fact2[1:]ltrim|trim('.') }}\"." }, { "category_id": 15, "poly": [ 307.0, 760.0, 1004.0, 760.0, 1004.0, 793.0, 307.0, 793.0 ], "score": 0.965, "text": "{% for choice in choices.label %} {% if choice" }, { "category_id": 15, "poly": [ 1047.0, 760.0, 1268.0, 760.0, 1268.0, 793.0, 1047.0, 793.0 ], "score": 0.992, "text": "answerKey %}{{" }, { "category_id": 15, "poly": [ 302.0, 786.0, 1147.0, 786.0, 1147.0, 820.0, 302.0, 820.0 ], "score": 0.989, "text": "answer_choices[loop.index - 1] }}{% endif %}{% endfor %}" }, { "category_id": 15, "poly": [ 301.0, 594.0, 957.0, 594.0, 957.0, 629.0, 301.0, 629.0 ], "score": 0.982, "text": "So， what's the best answer to the question?" }, { "category_id": 15, "poly": [ 294.0, 1703.0, 933.0, 1703.0, 933.0, 1741.0, 294.0, 1741.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 1739.0, 462.0, 1739.0, 462.0, 1771.0, 295.0, 1771.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 1575.0, 757.0, 1575.0, 757.0, 1609.0, 305.0, 1609.0 ], "score": 0.929, "text": "{{choices.text | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 306.0, 921.0, 757.0, 921.0, 757.0, 955.0, 306.0, 955.0 ], "score": 0.957, "text": "{{choices.text | join(\"lll\")}}" }, { "category_id": 15, "poly": [ 295.0, 861.0, 575.0, 861.0, 575.0, 901.0, 295.0, 901.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 301.0, 428.0, 1029.0, 428.0, 1029.0, 464.0, 301.0, 464.0 ], "score": 0.986, "text": "You are presented with the quiz \"{{ question }}\"" }, { "category_id": 15, "poly": [ 306.0, 241.0, 759.0, 241.0, 759.0, 277.0, 306.0, 277.0 ], "score": 0.943, "text": "{{choices.text l join(\"Ill\")}}" }, { "category_id": 15, "poly": [ 301.0, 1220.0, 538.0, 1220.0, 538.0, 1254.0, 301.0, 1254.0 ], "score": 1.0, "text": "Answer choices:" }, { "category_id": 15, "poly": [ 333.0, 1245.0, 865.0, 1245.0, 865.0, 1285.0, 333.0, 1285.0 ], "score": 0.96, "text": "{{answer_choices l join(\"\\n - \") }}" }, { "category_id": 15, "poly": [ 294.0, 1982.0, 475.0, 1982.0, 475.0, 2021.0, 294.0, 2021.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1515.0, 575.0, 1515.0, 575.0, 1555.0, 294.0, 1555.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 293.0, 1356.0, 474.0, 1356.0, 474.0, 1394.0, 293.0, 1394.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 702.0, 475.0, 702.0, 475.0, 740.0, 293.0, 740.0 ], "score": 0.986, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 370.0, 463.0, 370.0, 463.0, 408.0, 293.0, 408.0 ], "score": 0.999, "text": "Input Template:" } ], "page_info": { "page_no": 121, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 5, "poly": [ 296, 1489, 1364, 1489, 1364, 1829, 296, 1829 ], "score": 0.975, "html": "
KeyValue
answers['not enough information'，'to visit family'，'par...
contextThat fall came and I went back to Michigan and the...
context_idf001
correct_answer_id3
domainfiction
idf001_0
metadata{'author':'Joseph Devon'，'title': 'Black Eyed Su...
question question_idWhy was this character sent away after each school...
0
question_typeCausality
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{{choices.text I join("lll")}}
" }, { "category_id": 5, "poly": [ 301, 241, 1333, 241, 1333, 276, 301, 276 ], "score": 0.103, "html": "
{% ifanswerKeychoices.label[0] %} Yes 二{% else %} No {% endif %}
" }, { "category_id": 1, "poly": [ 298, 867, 468, 867, 468, 897, 298, 897 ], "score": 0.101 }, { "category_id": 13, "poly": [ 1005, 934, 1047, 934, 1047, 956, 1005, 956 ], "score": 0.79, "latex": "= =" }, { "category_id": 13, "poly": [ 540, 247, 583, 247, 583, 270, 540, 270 ], "score": 0.76, "latex": "= =" }, { "category_id": 15, "poly": [ 292.0, 1888.0, 408.0, 1888.0, 408.0, 1932.0, 292.0, 1932.0 ], "score": 1.0, "text": "Prompts" }, { "category_id": 15, "poly": [ 294.0, 1271.0, 471.0, 1271.0, 471.0, 1313.0, 294.0, 1313.0 ], "score": 1.0, "text": "1.7.6QUAIL" }, { "category_id": 15, "poly": [ 823.0, 2085.0, 878.0, 2085.0, 878.0, 2126.0, 823.0, 2126.0 ], "score": 1.0, "text": "123" }, { "category_id": 15, "poly": [ 295.0, 72.0, 816.0, 72.0, 816.0, 109.0, 295.0, 109.0 ], "score": 0.994, "text": "Published as a conference paper at ICLR 2022" }, { "category_id": 15, "poly": [ 293.0, 864.0, 474.0, 864.0, 474.0, 902.0, 293.0, 902.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 523.0, 463.0, 523.0, 463.0, 559.0, 294.0, 559.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1396.0, 472.0, 1396.0, 472.0, 1437.0, 295.0, 1437.0 ], "score": 0.999, "text": "Data Example" }, { "category_id": 15, "poly": [ 294.0, 321.0, 575.0, 321.0, 575.0, 361.0, 294.0, 361.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1035.0, 575.0, 1035.0, 575.0, 1075.0, 294.0, 1075.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 300.0, 386.0, 464.0, 386.0, 464.0, 421.0, 300.0, 421.0 ], "score": 0.917, "text": "Yes IIl No" }, { "category_id": 15, "poly": [ 294.0, 1325.0, 875.0, 1325.0, 875.0, 1371.0, 294.0, 1371.0 ], "score": 0.982, "text": "Dataset from Rogers et al. (2020). Used in training." }, { "category_id": 15, "poly": [ 295.0, 1969.0, 463.0, 1969.0, 463.0, 2005.0, 295.0, 2005.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 924.0, 1004.0, 924.0, 1004.0, 964.0, 305.0, 964.0 ], "score": 0.989, "text": "{% for choice in choices.label %} {% if choice" }, { "category_id": 15, "poly": [ 1048.0, 924.0, 1270.0, 924.0, 1270.0, 964.0, 1048.0, 964.0 ], "score": 0.995, "text": "answerKey %}{{" }, { "category_id": 15, "poly": [ 301.0, 953.0, 1148.0, 953.0, 1148.0, 992.0, 301.0, 992.0 ], "score": 0.992, "text": "answer_choices[loop.index - 1] }}{% endif %}{% endfor %}" }, { "category_id": 15, "poly": [ 299.0, 637.0, 1252.0, 637.0, 1252.0, 678.0, 299.0, 678.0 ], "score": 0.952, "text": "Fact 2: {{fact2[0]|lcapitalize }}{{ fact2[1:]ltrim|trim('.') }}." }, { "category_id": 15, "poly": [ 297.0, 581.0, 1268.0, 581.0, 1268.0, 622.0, 297.0, 622.0 ], "score": 0.953, "text": " Fact 1: {{ fact1[0]lcapitalize }}{{ 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KeyValue
idQuaRel_V1_Fr_0223
answer_index logical_forms1 ['(infer (speed higher worldl） (smoothness higher ...
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Key Value
answerKey choicesA {'label':['A'，'B']，'text':['scarce'，'plentifu...
id paraQRQA-10385-4
para_annoMany of the worlds people live with water scarcity... {'effect_prop':'population growth'，'cause_dir_st...
para_idQRSent-10385
question question_annoJohn's town used to have lots of water，back when {'more_effect_dir':'several thousand'，'less_effe...
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KeyValue
answerD
articleStudies show that you may be lied to every day any...
example_idhigh10001.txt
options question['harmful'，'easy'，'interesting'，'common'] From Para.1 we learn that lying is very
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(2017). Used in evaluation." }, { "category_id": 15, "poly": [ 300.0, 593.0, 409.0, 593.0, 409.0, 628.0, 300.0, 628.0 ], "score": 0.933, "text": "{%if'" }, { "category_id": 15, "poly": [ 508.0, 595.0, 730.0, 595.0, 730.0, 629.0, 508.0, 629.0 ], "score": 0.993, "text": "in question %}" }, { "category_id": 15, "poly": [ 303.0, 622.0, 868.0, 622.0, 868.0, 659.0, 303.0, 659.0 ], "score": 0.948, "text": "{{ question | trim(\".?!\") I replace(\"" }, { "category_id": 16, "poly": [ 936.0, 629.0, 953.0, 629.0, 953.0, 643.0, 936.0, 643.0 ], "score": 0.327, "text": "Ⅱ" }, { "category_id": 15, "poly": [ 954.0, 625.0, 1373.0, 625.0, 1373.0, 657.0, 954.0, 657.0 ], "score": 0.957, "text": "，answer_choices I join(\" or" }, { "category_id": 15, "poly": [ 344.0, 650.0, 539.0, 650.0, 539.0, 685.0, 344.0, 685.0 ], "score": 0.897, "text": "}}{{\"?\" }}" }, { "category_id": 15, "poly": [ 303.0, 676.0, 464.0, 676.0, 464.0, 714.0, 303.0, 714.0 ], "score": 0.928, "text": "{% else %}" }, { "category_id": 15, "poly": [ 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KeyValue
question What type of organism is commonly used in preparat...
distractor3 viruses
distractor1 protozoa
distractor2gymnosperms
correct_answer supportmesophilic organisms Mesophiles grow best in moderate temperature，typi...
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{% set order=[[0，1，2，3]，[0，1，3,2]，[0,2，1，3]，[0，2，3，1], [0，3，1，2]，[0，3，2，1],
[1，0，2，3]，[
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[2，1，0，3]，[2，1，0，2]，[2，2，
[2，0，3，1]，[2，3，1，0]，[2，0， 3，1， 0，31，
[3，1，2，0]，[3，1，0，2]，[3，1]，
[3，2，0，1]，[3，0，1，2]，[3，2，1， 0，2，0]， 1]]
Answer the following question given this paragraph:choice °}
{{support}}
Q:{{question}}
Choices:
- {{ answer_choices[order[0]] }}
-{{ answer_choices[order[1]] }}
-{{answer_choices[order[2]] }}
A:- {{ answer_choices[order[3]] }}
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KeyValue
answerAlike attending
answerBlike staying home
answerCa good friend to have
context labelCameron decided to have a barbecue and gathered he... 1
questionHow would Others feel as a result?
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{{question}}
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KeyValue
idx0
label1
passagePersian language -- Persian (/prn，-n/)，al...
questiondo iran and afghanistan speak the same language
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1083.0, 682.0, 1083.0, 682.0, 1115.0, 307.0, 1115.0 ], "score": 0.981, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 305.0, 1109.0, 475.0, 1109.0, 475.0, 1143.0, 305.0, 1143.0 ], "score": 0.96, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 1669.0, 499.0, 1669.0, 499.0, 1704.0, 305.0, 1704.0 ], "score": 0.98, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1669.0, 1012.0, 1669.0, 1012.0, 1704.0, 524.0, 1704.0 ], "score": 0.969, "text": " -1 %}{{ answer_choices[label] }}" }, { "category_id": 15, "poly": [ 306.0, 1700.0, 476.0, 1700.0, 476.0, 1732.0, 306.0, 1732.0 ], "score": 0.959, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 1474.0, 506.0, 1474.0, 506.0, 1505.0, 305.0, 1505.0 ], "score": 0.99, "text": "{{ passage }}" }, { "category_id": 15, "poly": [ 300.0, 1497.0, 1003.0, 1497.0, 1003.0, 1537.0, 300.0, 1537.0 ], "score": 0.986, "text": "Based on the previous passage, {{ question }}?" }, { "category_id": 15, "poly": [ 296.0, 1385.0, 705.0, 1385.0, 705.0, 1417.0, 296.0, 1417.0 ], "score": 0.982, "text": "Prompt from Schick and Schuitze (2021)" }, { "category_id": 15, "poly": [ 296.0, 1416.0, 462.0, 1416.0, 462.0, 1448.0, 296.0, 1448.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 300.0, 799.0, 1283.0, 799.0, 1283.0, 834.0, 300.0, 834.0 ], "score": 0.998, "text": "Exercise: read the text and answer the question by True or False." }, { "category_id": 15, "poly": [ 303.0, 856.0, 563.0, 856.0, 563.0, 890.0, 303.0, 890.0 ], "score": 0.982, "text": "Text: {{passage}}" }, { "category_id": 15, "poly": [ 304.0, 884.0, 657.0, 884.0, 657.0, 918.0, 304.0, 918.0 ], "score": 0.98, "text": "Question: {{question}}?" }, { "category_id": 15, "poly": [ 300.0, 242.0, 1222.0, 242.0, 1222.0, 278.0, 300.0, 278.0 ], "score": 0.969, "text": "Based on the following passage， {{ question }}? {{ passage }}" }, { "category_id": 15, "poly": [ 295.0, 1186.0, 575.0, 1186.0, 575.0, 1226.0, 295.0, 1226.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 545.0, 575.0, 545.0, 575.0, 585.0, 294.0, 585.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1776.0, 575.0, 1776.0, 575.0, 1816.0, 294.0, 1816.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 740.0, 463.0, 740.0, 463.0, 777.0, 294.0, 777.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 994.0, 474.0, 994.0, 474.0, 1032.0, 293.0, 1032.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 300.0, 1248.0, 523.0, 1248.0, 523.0, 1282.0, 300.0, 1282.0 ], "score": 0.976, "text": "False IIl True" }, { "category_id": 15, "poly": [ 293.0, 1611.0, 474.0, 1611.0, 474.0, 1650.0, 293.0, 1650.0 ], "score": 0.999, "text": "Target Template:" } ], "page_info": { "page_no": 148, "width": 1700, "height": 2200 } }, { "layout_dets": [ { 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KeyValue
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paragraph question0 While this process moved along，diplomacy continue... What did the high-level effort to persuade Pakista...
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1738.0, 463.0, 1738.0, 463.0, 1774.0, 295.0, 1774.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 580.0, 463.0, 580.0, 463.0, 616.0, 295.0, 616.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1419.0, 475.0, 1419.0, 475.0, 1458.0, 294.0, 1458.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 393.0, 574.0, 393.0, 574.0, 428.0, 295.0, 428.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 298.0, 446.0, 464.0, 446.0, 464.0, 485.0, 298.0, 485.0 ], "score": 0.916, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 295.0, 985.0, 574.0, 985.0, 574.0, 1020.0, 295.0, 1020.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1983.0, 475.0, 1983.0, 475.0, 2022.0, 294.0, 2022.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 298.0, 1038.0, 464.0, 1038.0, 464.0, 1077.0, 298.0, 1077.0 ], "score": 0.903, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 299.0, 1605.0, 464.0, 1605.0, 464.0, 1644.0, 299.0, 1644.0 ], "score": 0.933, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 304.0, 1257.0, 536.0, 1257.0, 536.0, 1291.0, 304.0, 1291.0 ], "score": 0.973, "text": "{{ paragraph }}" }, { "category_id": 15, "poly": [ 300.0, 1282.0, 676.0, 1282.0, 676.0, 1324.0, 300.0, 1324.0 ], "score": 0.965, "text": "Question: {{ question }}" }, { "category_id": 15, "poly": [ 297.0, 1305.0, 841.0, 1305.0, 841.0, 1350.0, 297.0, 1350.0 ], "score": 0.973, "text": "Is the correct answer {{ answer }]?" }, { "category_id": 15, "poly": [ 304.0, 906.0, 498.0, 906.0, 498.0, 946.0, 304.0, 946.0 ], "score": 0.977, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 906.0, 1148.0, 906.0, 1148.0, 946.0, 524.0, 946.0 ], "score": 0.981, "text": " -1 %}{{answer_choices[label]}}{% endif %}" }, { "category_id": 15, "poly": [ 306.0, 316.0, 499.0, 316.0, 499.0, 351.0, 306.0, 351.0 ], "score": 0.972, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 316.0, 1177.0, 316.0, 1177.0, 351.0, 524.0, 351.0 ], "score": 0.968, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 1552.0, 574.0, 1552.0, 574.0, 1587.0, 295.0, 1587.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 296.0, 1173.0, 705.0, 1173.0, 705.0, 1205.0, 296.0, 1205.0 ], "score": 0.982, "text": "Prompt from Schick and Schuitze (2021)" }, { "category_id": 15, "poly": [ 296.0, 1204.0, 462.0, 1204.0, 462.0, 1236.0, 296.0, 1236.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 1475.0, 498.0, 1475.0, 498.0, 1511.0, 306.0, 1511.0 ], "score": 0.988, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1475.0, 1176.0, 1475.0, 1176.0, 1511.0, 524.0, 1511.0 ], "score": 0.983, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 301.0, 687.0, 1392.0, 687.0, 1392.0, 723.0, 301.0, 723.0 ], "score": 0.991, "text": "Decide whether \"{{answer}}\" is a valid answer to the following question:" }, { "category_id": 15, "poly": [ 305.0, 719.0, 491.0, 719.0, 491.0, 752.0, 305.0, 752.0 ], "score": 0.999, "text": "{{question}}" }, { "category_id": 15, "poly": [ 300.0, 744.0, 565.0, 744.0, 565.0, 780.0, 300.0, 780.0 ], "score": 0.968, "text": " Answer yes or no." }, { "category_id": 15, "poly": [ 299.0, 1793.0, 1196.0, 1793.0, 1196.0, 1827.0, 299.0, 1827.0 ], "score": 0.981, "text": " Is \"{{answer}}\" a correct answer to the following question?" }, { "category_id": 15, "poly": [ 301.0, 1819.0, 640.0, 1819.0, 640.0, 1856.0, 301.0, 1856.0 ], "score": 0.987, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 302.0, 1879.0, 922.0, 1879.0, 922.0, 1910.0, 302.0, 1910.0 ], "score": 0.993, "text": "Rely on the following text: {{paragraph}}" }, { "category_id": 15, "poly": [ 295.0, 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No IIl Yes
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Does that sound right?" }, { "category_id": 15, "poly": [ 293.0, 1726.0, 863.0, 1726.0, 863.0, 1775.0, 293.0, 1775.0 ], "score": 0.992, "text": "Dataset from Welbl et al. (2018). Used in training." }, { "category_id": 15, "poly": [ 294.0, 1973.0, 464.0, 1973.0, 464.0, 2009.0, 294.0, 2009.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 1373.0, 498.0, 1373.0, 498.0, 1411.0, 304.0, 1411.0 ], "score": 0.974, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 1373.0, 1178.0, 1373.0, 1178.0, 1411.0, 524.0, 1411.0 ], "score": 0.969, "text": " -1 %}{{ answer_choices[label] }}{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 819.0, 498.0, 819.0, 498.0, 857.0, 304.0, 857.0 ], "score": 0.971, "text": "{% if label !" }, { "category_id": 15, "poly": [ 524.0, 819.0, 1148.0, 819.0, 1148.0, 857.0, 524.0, 857.0 ], "score": 0.988, "text": " -1 %}{{answer_choices[label]}}{% endif %}" }, { "category_id": 15, "poly": [ 304.0, 239.0, 1148.0, 239.0, 1148.0, 278.0, 304.0, 278.0 ], "score": 0.987, "text": "{% if label != -1 %}{{answer_choices[label]}}{% endif %}" }, { "category_id": 15, "poly": [ 295.0, 321.0, 574.0, 321.0, 574.0, 356.0, 295.0, 356.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 1451.0, 575.0, 1451.0, 575.0, 1491.0, 294.0, 1491.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 898.0, 575.0, 898.0, 575.0, 938.0, 294.0, 938.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 512.0, 463.0, 512.0, 463.0, 548.0, 295.0, 548.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 303.0, 624.0, 639.0, 624.0, 639.0, 660.0, 303.0, 660.0 ], "score": 0.991, "text": "Question: {{question}}" }, { "category_id": 15, "poly": [ 299.0, 649.0, 1347.0, 649.0, 1347.0, 689.0, 299.0, 689.0 ], "score": 0.993, "text": "I think \"{{answer}}\" is a valid answer. Could you confirm? Yes or no?" }, { "category_id": 15, "poly": [ 293.0, 1316.0, 474.0, 1316.0, 474.0, 1355.0, 293.0, 1355.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 306.0, 570.0, 505.0, 570.0, 505.0, 606.0, 306.0, 606.0 ], "score": 0.999, "text": "{{paragraph}}" }, { "category_id": 15, "poly": [ 293.0, 763.0, 474.0, 763.0, 474.0, 801.0, 293.0, 801.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 298.0, 376.0, 464.0, 376.0, 464.0, 414.0, 298.0, 414.0 ], "score": 0.892, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 295.0, 1093.0, 463.0, 1093.0, 463.0, 1129.0, 295.0, 1129.0 ], "score": 0.999, "text": "Input Template:" } ], "page_info": { "page_no": 152, "width": 1700, "height": 2200 } }, { "layout_dets": [ { "category_id": 1, "poly": [ 297, 1818, 931, 1818, 931, 1878, 297, 1878 ], "score": 0.928 }, { "category_id": 5, "poly": [ 298, 226, 1299, 226, 1299, 450, 298, 450 ], "score": 0.927, "html": "
Key Value
annotations[]
answer1996 summer olympics
candidates['1996 summer olympics'，'olympic games'，'sport']
id questionWH_train_0 participant_of juan rossell
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metadata-path_len2
metadata_question_idinfluence_graph:1217:144:106#0
metadata_question_typeINPARA_EFFECT
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KeyValue
goalWhen boiling butter，when it's ready，you can
label1
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0.983, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 307.0, 434.0, 697.0, 434.0, 697.0, 465.0, 307.0, 465.0 ], "score": 0.977, "text": "{{\"Solution 1\"}}:{{soll}}" }, { "category_id": 15, "poly": [ 307.0, 462.0, 699.0, 462.0, 699.0, 493.0, 307.0, 493.0 ], "score": 0.992, "text": "{{\"Solution 2\"}}:{{sol2}}" }, { "category_id": 15, "poly": [ 306.0, 1518.0, 683.0, 1518.0, 683.0, 1551.0, 306.0, 1551.0 ], "score": 0.977, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 307.0, 935.0, 622.0, 935.0, 622.0, 967.0, 307.0, 967.0 ], "score": 0.92, "text": "{{soll}}IIl{{sol2}}" }, { "category_id": 15, "poly": [ 295.0, 875.0, 574.0, 875.0, 574.0, 910.0, 295.0, 910.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 300.0, 1875.0, 1257.0, 1875.0, 1257.0, 1909.0, 300.0, 1909.0 ], "score": 0.989, "text": "Given a goal and a wrong solution, rewrite it to give a correct" }, { "category_id": 15, "poly": [ 300.0, 1901.0, 444.0, 1901.0, 444.0, 1936.0, 300.0, 1936.0 ], "score": 1.0, "text": "solution." }, { "category_id": 15, "poly": [ 299.0, 1929.0, 521.0, 1929.0, 521.0, 1969.0, 299.0, 1969.0 ], "score": 0.997, "text": "Goal:{{goal}}" }, { "category_id": 15, "poly": [ 298.0, 1954.0, 865.0, 1954.0, 865.0, 1998.0, 298.0, 1998.0 ], "score": 0.932, "text": "Solution:{{[soll, sol2][1 - label]]]" }, { "category_id": 15, "poly": [ 302.0, 1987.0, 594.0, 1987.0, 594.0, 2017.0, 302.0, 2017.0 ], "score": 0.997, "text": "Corrected solution:" }, { "category_id": 15, "poly": [ 304.0, 629.0, 881.0, 629.0, 881.0, 660.0, 304.0, 660.0 ], "score": 0.994, "text": "Answer by copying the correct solution" }, { "category_id": 15, "poly": [ 302.0, 516.0, 520.0, 516.0, 520.0, 552.0, 302.0, 552.0 ], "score": 0.997, "text": "Goal:{{goal}}" }, { "category_id": 15, "poly": [ 302.0, 1237.0, 877.0, 1237.0, 877.0, 1270.0, 302.0, 1270.0 ], "score": 0.97, "text": "Choice {{answer_choices[1]}}: {{sol2}}" }, { "category_id": 15, "poly": [ 295.0, 375.0, 464.0, 375.0, 464.0, 411.0, 295.0, 411.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 308.0, 243.0, 621.0, 243.0, 621.0, 275.0, 308.0, 275.0 ], "score": 0.918, "text": "{{soll}}IIl{{sol2}}" }, { "category_id": 15, "poly": [ 302.0, 573.0, 987.0, 573.0, 987.0, 605.0, 302.0, 605.0 ], "score": 0.986, "text": "Given the goal， what is the correct solution?" }, { "category_id": 15, "poly": [ 301.0, 1345.0, 417.0, 1345.0, 417.0, 1381.0, 301.0, 1381.0 ], "score": 1.0, "text": "Answer:" }, { "category_id": 15, "poly": [ 299.0, 1287.0, 1331.0, 1287.0, 1331.0, 1327.0, 299.0, 1327.0 ], "score": 0.993, "text": " What is the index of the correct choice for ending for the sentence?" }, { "category_id": 15, "poly": [ 304.0, 1180.0, 878.0, 1180.0, 878.0, 1215.0, 304.0, 1215.0 ], "score": 0.962, "text": "Choice {{answer_choices[0]}}: {{sol1}}" }, { "category_id": 15, "poly": [ 295.0, 1595.0, 574.0, 1595.0, 574.0, 1630.0, 295.0, 1630.0 ], "score": 0.992, 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298.0, 293.0, 298.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 431.0, 463.0, 431.0, 463.0, 467.0, 294.0, 467.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 297.0, 1525.0, 465.0, 1525.0, 465.0, 1566.0, 297.0, 1566.0 ], "score": 0.918, "text": "No IIl Yes" }, { "category_id": 15, "poly": [ 293.0, 1329.0, 474.0, 1329.0, 474.0, 1367.0, 293.0, 1367.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 1663.0, 463.0, 1663.0, 463.0, 1699.0, 294.0, 1699.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 293.0, 1971.0, 475.0, 1971.0, 475.0, 2009.0, 293.0, 2009.0 ], "score": 0.986, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 767.0, 474.0, 767.0, 474.0, 805.0, 293.0, 805.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1466.0, 575.0, 1466.0, 575.0, 1506.0, 295.0, 1506.0 ], "score": 0.98, 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15, "poly": [ 305.0, 1190.0, 565.0, 1190.0, 565.0, 1225.0, 305.0, 1225.0 ], "score": 0.973, "text": "{{goal}}{{sol2}}" }, { "category_id": 15, "poly": [ 303.0, 1220.0, 732.0, 1220.0, 732.0, 1252.0, 303.0, 1252.0 ], "score": 0.999, "text": "Does this phrase make sense?" }, { "category_id": 15, "poly": [ 306.0, 829.0, 683.0, 829.0, 683.0, 862.0, 306.0, 862.0 ], "score": 0.977, "text": "{{answer_choices[label]}}" }, { "category_id": 15, "poly": [ 295.0, 904.0, 575.0, 904.0, 575.0, 944.0, 295.0, 944.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 306.0, 321.0, 653.0, 321.0, 653.0, 355.0, 306.0, 355.0 ], "score": 0.946, "text": "{{[sol1，sol2][label]}}" }, { "category_id": 15, "poly": [ 300.0, 489.0, 1208.0, 489.0, 1208.0, 526.0, 300.0, 526.0 ], "score": 0.984, "text": " Finish the following sentence with the best choice: {{goal}}" }, { "category_id": 15, "poly": [ 305.0, 1387.0, 684.0, 1387.0, 684.0, 1424.0, 305.0, 1424.0 ], "score": 0.976, "text": 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KeyValue
content This sound track was beautiful! It paints the sene...
1
label titleStuning even for the non-gamer
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KeyValue
dateOctober 12 2016
package_namecom.mantz_it.rfanalyzer
review starGreat app! The new version now works on my Bravia ...
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unflattering Ill flattering
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{{star}}
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Key Value
text labelBromwell High is a cartoon comedy. It ran at the s... 1
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1964.0 ], "score": 0.971, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 577.0, 475.0, 577.0, 475.0, 615.0, 294.0, 615.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 1733.0, 574.0, 1733.0, 574.0, 1768.0, 295.0, 1768.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1226.0, 574.0, 1226.0, 574.0, 1261.0, 295.0, 1261.0 ], "score": 0.992, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 1420.0, 463.0, 1420.0, 463.0, 1456.0, 295.0, 1456.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 374.0, 932.0, 374.0, 932.0, 412.0, 294.0, 412.0 ], "score": 0.994, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 409.0, 462.0, 409.0, 462.0, 441.0, 295.0, 441.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 974.0, 942.0, 974.0, 942.0, 1009.0, 305.0, 1009.0 ], "score": 0.992, "text": "{{text}} Did the reviewer enjoy the movie?" }, { "category_id": 15, "poly": [ 306.0, 466.0, 808.0, 466.0, 808.0, 501.0, 306.0, 501.0 ], "score": 0.979, "text": "{{text}} This is definitely not a" }, { "category_id": 15, "poly": [ 306.0, 1986.0, 1092.0, 1986.0, 1092.0, 2021.0, 306.0, 2021.0 ], "score": 0.99, "text": "{{text}} How does the reviewer feel about the movie?" }, { "category_id": 15, "poly": [ 303.0, 1473.0, 1378.0, 1473.0, 1378.0, 1516.0, 303.0, 1516.0 ], "score": 0.993, "text": "{{text}} What is the sentiment expressed by the reviewer for the movie?" }, { "category_id": 15, "poly": [ 305.0, 638.0, 863.0, 638.0, 863.0, 674.0, 305.0, 674.0 ], "score": 0.977, "text": "{{ answer_choices [1-label]}} review." }, { "category_id": 15, "poly": [ 306.0, 1651.0, 729.0, 1651.0, 729.0, 1688.0, 306.0, 1688.0 ], "score": 0.951, "text": "{{ answer_choices [label] }}" }, { "category_id": 15, "poly": [ 306.0, 1144.0, 729.0, 1144.0, 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KeyValue
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textdr. goldberg offers everything i look for in a gen...
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KeyValue
activity_label ctxRemoving ice from car Then，the man writes over the snow covering the wi...
ctx_aThen， the man writes over the snow covering the wi...
ctx_b endingsthen
ind['，the man adds wax to the windshield and cuts it...
label source_id4 3
splitactivitynet~v_-1IBHYS3L-Y
split_typetrain indomain
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{{choicel}} IIl {{choice2}}
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{{endings | join(" Ill ")}}
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KeyValue
concept_set_idx
concepts['ski'，'mountain'，'skier']
targetSkier skis down the mountain
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Ending 1 IIl Ending 2 IIl Ending 3 IIl Ending 4
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{{target}}
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Key Value
input_text target_text{'table': {'column_header':['name'，'nationality'... walter extra is a german award-winning aerobatic p...
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301.0, 558.0 ], "score": 0.999, "text": "Facts:" }, { "category_id": 15, "poly": [ 293.0, 854.0, 474.0, 854.0, 474.0, 892.0, 293.0, 892.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 293.0, 1983.0, 475.0, 1983.0, 475.0, 2021.0, 293.0, 2021.0 ], "score": 0.986, "text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 470.0, 463.0, 470.0, 463.0, 506.0, 295.0, 506.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 303.0, 1104.0, 986.0, 1104.0, 986.0, 1136.0, 303.0, 1136.0 ], "score": 0.99, "text": "Read the bio below and try to give details on" }, { "category_id": 15, "poly": [ 307.0, 1132.0, 731.0, 1132.0, 731.0, 1166.0, 307.0, 1166.0 ], "score": 0.995, "text": "{{input_text[\"context\"]}}'s:" }, { "category_id": 15, "poly": [ 305.0, 1160.0, 1392.0, 1160.0, 1392.0, 1193.0, 305.0, 1193.0 ], "score": 0.974, "text": "{% for n in range (input_text[\"table\"][\"column_header\"]llength) %} {% if" }, { "category_id": 15, "poly": [ 303.0, 1187.0, 912.0, 1187.0, 912.0, 1224.0, 303.0, 1224.0 ], "score": 0.986, "text": "input_text[\"table\"][\"column_header\"][n]" }, { "category_id": 15, "poly": [ 942.0, 1187.0, 1227.0, 1187.0, 1227.0, 1224.0, 942.0, 1224.0 ], "score": 0.968, "text": "\"article_title\" %}" }, { "category_id": 15, "poly": [ 304.0, 1213.0, 1123.0, 1213.0, 1123.0, 1248.0, 304.0, 1248.0 ], "score": 0.967, "text": "- {{ input_text[\"table\"][\"column_header\"][n].replace(\"_" }, { "category_id": 15, "poly": [ 1130.0, 1219.0, 1154.0, 1219.0, 1154.0, 1242.0, 1130.0, 1242.0 ], "score": 0.697, "text": "\"," }, { "category_id": 15, "poly": [ 1156.0, 1218.0, 1177.0, 1218.0, 1177.0, 1236.0, 1156.0, 1236.0 ], "score": 0.84, "text": "\"" }, { "category_id": 15, "poly": [ 1218.0, 1217.0, 1267.0, 1217.0, 1267.0, 1248.0, 1218.0, 1248.0 ], "score": 0.997, "text": "}}" }, { "category_id": 15, "poly": [ 304.0, 1241.0, 670.0, 1241.0, 670.0, 1277.0, 304.0, 1277.0 ], "score": 0.915, "text": "{% endif %} {% 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1298.0, 586.0, 307.0, 586.0 ], "score": 0.983, "text": "{% for n in range (input_text[\"table\"][\"column_header\"]|length) %}" }, { "category_id": 15, "poly": [ 306.0, 581.0, 1003.0, 581.0, 1003.0, 616.0, 306.0, 616.0 ], "score": 0.986, "text": "{%if input_text[\"table\"][\"column_header\"][n]" }, { "category_id": 15, "poly": [ 1033.0, 581.0, 1313.0, 581.0, 1313.0, 616.0, 1033.0, 616.0 ], "score": 0.979, "text": "\"article_title\" %}" }, { "category_id": 15, "poly": [ 303.0, 604.0, 1269.0, 604.0, 1269.0, 644.0, 303.0, 644.0 ], "score": 0.967, "text": "-{{input_text[\"table\"][\"column_header\"][n].replace(\"_\",\" \") }}:" }, { "category_id": 15, "poly": [ 306.0, 636.0, 878.0, 636.0, 878.0, 670.0, 306.0, 670.0 ], "score": 0.975, "text": "{{input_text[\"table\"][\"content\"][n] }}" }, { "category_id": 15, "poly": [ 306.0, 663.0, 477.0, 663.0, 477.0, 698.0, 306.0, 698.0 ], "score": 0.932, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 305.0, 690.0, 489.0, 690.0, 489.0, 725.0, 305.0, 725.0 ], "score": 0.93, "text": "{% endfor %}" }, { "category_id": 15, "poly": [ 303.0, 721.0, 1330.0, 721.0, 1330.0, 752.0, 303.0, 752.0 ], "score": 0.99, "text": "Based on these bullet points, write a short biography describing the" }, { "category_id": 15, "poly": [ 301.0, 748.0, 817.0, 748.0, 817.0, 782.0, 301.0, 782.0 ], "score": 0.987, "text": "life of {{input_text[\"context\"]}}." }, { "category_id": 15, "poly": [ 301.0, 1875.0, 608.0, 1875.0, 608.0, 1909.0, 301.0, 1909.0 ], "score": 0.997, "text": "Bio:{{target_text}}" }, { "category_id": 15, "poly": [ 301.0, 1793.0, 1332.0, 1793.0, 1332.0, 1827.0, 301.0, 1827.0 ], "score": 0.992, "text": " What type of details about {{input_text[\"context\"]}} can be gathered" }, { "category_id": 15, "poly": [ 302.0, 1822.0, 655.0, 1822.0, 655.0, 1853.0, 302.0, 1853.0 ], "score": 0.997, "text": "from the following bio?" }, { "category_id": 15, "poly": [ 295.0, 470.0, 463.0, 470.0, 463.0, 506.0, 295.0, 506.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 1461.0, 1294.0, 1461.0, 1294.0, 1495.0, 306.0, 1495.0 ], "score": 0.981, "text": "{% for n in range (input_text[\"table\"][\"column_header\"]llength) %}" }, { "category_id": 15, "poly": [ 304.0, 1488.0, 1005.0, 1488.0, 1005.0, 1521.0, 304.0, 1521.0 ], "score": 0.979, "text": "{%if input_text[\"table\"][\"column_header\"][n]!" }, { "category_id": 15, "poly": [ 1031.0, 1488.0, 1312.0, 1488.0, 1312.0, 1521.0, 1031.0, 1521.0 ], "score": 0.966, "text": " \"article_title\" %}" }, { "category_id": 15, "poly": [ 330.0, 1516.0, 1121.0, 1516.0, 1121.0, 1550.0, 330.0, 1550.0 ], "score": 0.975, "text": "{{ input_text[\"table\"][\"column_header\"][n].replace(\"." }, { "category_id": 15, "poly": [ 1145.0, 1536.0, 1156.0, 1536.0, 1156.0, 1545.0, 1145.0, 1545.0 ], "score": 0.613, "text": "/" }, { "category_id": 15, "poly": [ 1214.0, 1518.0, 1359.0, 1518.0, 1359.0, 1549.0, 1214.0, 1549.0 ], "score": 0.99, "text": "}}is{{" }, { "category_id": 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highlights Syrian official: Obama climbed to the top of the t...
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{{highlights}}
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{{highlights}}
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KeyValue
documentaustralia 's current account deficit shrunk by a r...
summaryaustralian current account deficit narrows sharply
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KeyValue
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KeyValue
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id summary13818513 Amanda baked cookies and will bring Jerry some tom...
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"text": "Target Template:" }, { "category_id": 15, "poly": [ 295.0, 351.0, 463.0, 351.0, 463.0, 388.0, 295.0, 388.0 ], "score": 0.972, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1049.0, 463.0, 1049.0, 463.0, 1086.0, 295.0, 1086.0 ], "score": 0.972, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1777.0, 463.0, 1777.0, 463.0, 1814.0, 295.0, 1814.0 ], "score": 0.972, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1973.0, 474.0, 1973.0, 474.0, 2009.0, 294.0, 2009.0 ], "score": 0.999, "text": "Target Template:" }, { "category_id": 15, "poly": [ 302.0, 1106.0, 864.0, 1106.0, 864.0, 1139.0, 302.0, 1139.0 ], "score": 0.999, "text": "Generate a summary for this dialogue:" }, { "category_id": 15, "poly": [ 308.0, 1137.0, 485.0, 1137.0, 485.0, 1166.0, 308.0, 1166.0 ], "score": 0.999, "text": "{{dialogue}}" }, { "category_id": 15, "poly": [ 294.0, 1411.0, 933.0, 1411.0, 933.0, 1447.0, 294.0, 1447.0 ], "score": 0.997, "text": "Prompt 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KeyValue
document Recent reports have linked some France-based playe...
id29750031
summaryNew Welsh Rugby Union chairman Gareth Davies belie...
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{{summary}}
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Used in evaluation." }, { "category_id": 15, "poly": [ 295.0, 883.0, 463.0, 883.0, 463.0, 919.0, 295.0, 919.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 305.0, 939.0, 490.0, 939.0, 490.0, 977.0, 305.0, 977.0 ], "score": 0.998, "text": "{{document}}" }, { "category_id": 15, "poly": [ 301.0, 1053.0, 833.0, 1053.0, 833.0, 1087.0, 301.0, 1087.0 ], "score": 0.986, "text": "Write a summary of the text above :" }, { "category_id": 15, "poly": [ 304.0, 1832.0, 490.0, 1832.0, 490.0, 1863.0, 304.0, 1863.0 ], "score": 0.999, "text": "{{document}}" }, { "category_id": 15, "poly": [ 300.0, 1856.0, 956.0, 1856.0, 956.0, 1893.0, 300.0, 1893.0 ], "score": 0.982, "text": " How would you rephrase that in a few words?" }, { "category_id": 15, "poly": [ 301.0, 1452.0, 432.0, 1452.0, 432.0, 1491.0, 301.0, 1491.0 ], "score": 0.999, "text": "Summary:" }, { "category_id": 15, "poly": [ 294.0, 1736.0, 614.0, 1736.0, 614.0, 1775.0, 294.0, 1775.0 ], "score": 0.99, "text": 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1284.0, 294.0, 1284.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1970.0, 463.0, 1970.0, 463.0, 2006.0, 295.0, 2006.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 302.0, 1655.0, 862.0, 1655.0, 862.0, 1688.0, 302.0, 1688.0 ], "score": 0.985, "text": " Summarize this document: {{document}}" }, { "category_id": 15, "poly": [ 303.0, 1684.0, 431.0, 1684.0, 431.0, 1719.0, 303.0, 1719.0 ], "score": 0.965, "text": " Summary :" }, { "category_id": 15, "poly": [ 305.0, 933.0, 492.0, 933.0, 492.0, 968.0, 305.0, 968.0 ], "score": 0.999, "text": "{{document}}" }, { "category_id": 15, "poly": [ 303.0, 961.0, 895.0, 961.0, 895.0, 996.0, 303.0, 996.0 ], "score": 1.0, "text": "This boils down to the simple idea that" }, { "category_id": 15, "poly": [ 301.0, 556.0, 833.0, 556.0, 833.0, 590.0, 301.0, 590.0 ], "score": 0.99, "text": "So I recapped it in layman's terms:" }, { "category_id": 15, "poly": [ 304.0, 1310.0, 652.0, 1310.0, 652.0, 1341.0, 304.0, 1341.0 ], "score": 0.979, "text": "Summarize: {{document}}" }, { "category_id": 15, "poly": [ 299.0, 444.0, 1105.0, 444.0, 1105.0, 479.0, 299.0, 479.0 ], "score": 0.985, "text": " My college roommate asked me what this article means:" }, { "category_id": 15, "poly": [ 307.0, 502.0, 489.0, 502.0, 489.0, 534.0, 307.0, 534.0 ], "score": 0.998, "text": "{{document}}" }, { "category_id": 15, "poly": [ 304.0, 1855.0, 475.0, 1855.0, 475.0, 1894.0, 304.0, 1894.0 ], "score": 0.994, "text": "{{summary}}" }, { "category_id": 15, "poly": [ 294.0, 353.0, 614.0, 353.0, 614.0, 390.0, 294.0, 390.0 ], "score": 0.999, "text": "Prompt from Brockman (2020)" }, { "category_id": 15, "poly": [ 295.0, 387.0, 462.0, 387.0, 462.0, 419.0, 295.0, 419.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 1482.0, 474.0, 1482.0, 474.0, 1517.0, 304.0, 1517.0 ], "score": 0.993, "text": "{{summary}}" }, { "category_id": 15, "poly": [ 295.0, 1970.0, 463.0, 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Key Value
text labelWall St. Bears Claw Back Into the Black (Reuters) ... 2
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{{summary}}
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(2015b). Used in training." }, { "category_id": 15, "poly": [ 302.0, 1268.0, 401.0, 1268.0, 401.0, 1304.0, 302.0, 1304.0 ], "score": 0.964, "text": "TL; DR:" }, { "category_id": 15, "poly": [ 294.0, 1116.0, 647.0, 1116.0, 647.0, 1154.0, 294.0, 1154.0 ], "score": 0.993, "text": "Prompt from Radford et al. 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content Abbott of Farnham E D Abbott Limited was a Britis...
label title0 E．D.Abbott Ltd
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"Input Template:" }, { "category_id": 15, "poly": [ 295.0, 1575.0, 573.0, 1575.0, 573.0, 1612.0, 295.0, 1612.0 ], "score": 0.99, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 316.0, 575.0, 316.0, 575.0, 356.0, 295.0, 356.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 295.0, 944.0, 575.0, 944.0, 575.0, 984.0, 295.0, 984.0 ], "score": 0.98, "text": "Answer Choices Template:" }, { "category_id": 15, "poly": [ 293.0, 1440.0, 474.0, 1440.0, 474.0, 1478.0, 293.0, 1478.0 ], "score": 0.998, "text": "Target Template:" }, { "category_id": 15, "poly": [ 303.0, 617.0, 1267.0, 617.0, 1267.0, 657.0, 303.0, 657.0 ], "score": 0.985, "text": "{{title}} - {{content}} Given a choice of categories {{\"company," }, { "category_id": 15, "poly": [ 301.0, 646.0, 1272.0, 646.0, 1272.0, 681.0, 301.0, 681.0 ], "score": 0.971, "text": "educational institution，artist，athlete，office holder，mean of" }, { "category_id": 15, "poly": [ 302.0, 677.0, 1368.0, 677.0, 1368.0, 710.0, 302.0, 710.0 ], "score": 0.959, "text": "transportation, building， natural place, village，animal, plant，album," }, { "category_id": 15, "poly": [ 303.0, 703.0, 1119.0, 703.0, 1119.0, 737.0, 303.0, 737.0 ], "score": 0.987, "text": "film or written work\"}}， the text refers to which one?" }, { "category_id": 15, "poly": [ 305.0, 1249.0, 1267.0, 1249.0, 1267.0, 1282.0, 305.0, 1282.0 ], "score": 0.983, "text": "\"{{title}}\"， given a list of categories: {{\"company， educational" }, { "category_id": 15, "poly": [ 305.0, 1277.0, 1325.0, 1277.0, 1325.0, 1310.0, 305.0, 1310.0 ], "score": 0.961, "text": "institution, artist，athlete, office holder, mean of transportation," }, { "category_id": 15, "poly": [ 303.0, 1305.0, 1374.0, 1305.0, 1374.0, 1338.0, 303.0, 1338.0 ], "score": 0.956, "text": "building， natural place, village， animal, plant，album, film or written" }, { "category_id": 15, "poly": [ 304.0, 1332.0, 1030.0, 1332.0, 1030.0, 1366.0, 304.0, 1366.0 ], "score": 0.983, "text": "work\"}}， what category does the title belong to?" }, { "category_id": 15, "poly": [ 302.0, 1004.0, 1347.0, 1004.0, 1347.0, 1040.0, 302.0, 1040.0 ], "score": 0.965, "text": "Company IIl Educational Institution IIl Artist IIl Athlete Ill Office" }, { "category_id": 15, "poly": [ 300.0, 1030.0, 1334.0, 1030.0, 1334.0, 1068.0, 300.0, 1068.0 ], "score": 0.95, "text": "Holder\" Iil Mean Of Transportation Ill Building Iil Natural Place Ill" }, { "category_id": 15, "poly": [ 301.0, 1060.0, 1272.0, 1060.0, 1272.0, 1096.0, 301.0, 1096.0 ], "score": 0.94, "text": "Village IIl Animal IIl Plant IIl Album IIl Film IIl Written Work" }, { "category_id": 15, "poly": [ 302.0, 1631.0, 1348.0, 1631.0, 1348.0, 1668.0, 302.0, 1668.0 ], "score": 0.955, "text": "Company IIl Educational Institution IlIl Artist IIl Athlete Ill Office" }, { "category_id": 15, "poly": [ 299.0, 1659.0, 1327.0, 1659.0, 1327.0, 1697.0, 299.0, 1697.0 ], "score": 0.951, "text": "Holder\"Iil Mean Of Transportation Ill Building Iil Natural Place Ill" }, { "category_id": 15, "poly": [ 300.0, 1687.0, 1272.0, 1687.0, 1272.0, 1723.0, 300.0, 1723.0 ], "score": 0.933, "text": "Village Iil Animal IIl Plant IIl Aibum IIl Fiim iil Written Work" }, { "category_id": 15, "poly": [ 293.0, 1899.0, 870.0, 1899.0, 870.0, 1947.0, 293.0, 1947.0 ], "score": 0.992, "text": "Datase from Li and Roth (2002). 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1264.0, 462.0, 1296.0, 295.0, 1296.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 306.0, 1573.0, 973.0, 1573.0, 973.0, 1604.0, 306.0, 1604.0 ], "score": 0.99, "text": "{{answer_choices[label_mapping[label_fine]]" }, { "category_id": 15, "poly": [ 974.0, 1579.0, 1008.0, 1579.0, 1008.0, 1601.0, 974.0, 1601.0 ], "score": 0.999, "text": "}}" }, { "category_id": 15, "poly": [ 305.0, 1602.0, 475.0, 1602.0, 475.0, 1634.0, 305.0, 1634.0 ], "score": 0.984, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 307.0, 319.0, 1026.0, 319.0, 1026.0, 354.0, 307.0, 354.0 ], "score": 0.975, "text": "{{answer_choices [label_mapping[label_fine]] }}" }, { "category_id": 15, "poly": [ 305.0, 347.0, 475.0, 347.0, 475.0, 381.0, 305.0, 381.0 ], "score": 0.957, "text": "{% endif %}" }, { "category_id": 15, "poly": [ 307.0, 932.0, 834.0, 932.0, 834.0, 967.0, 307.0, 967.0 ], "score": 0.982, "text": "{{ answer_choices [label_coarse] }}" }, { "category_id": 15, "poly": [ 301.0, 485.0, 1182.0, 485.0, 1182.0, 520.0, 301.0, 520.0 ], "score": 0.952, "text": "city IIl country Ill mountain Ill state Ill other location" }, { "category_id": 15, "poly": [ 302.0, 1986.0, 1106.0, 1986.0, 1106.0, 2022.0, 302.0, 2022.0 ], "score": 0.984, "text": "Is this asking about {{('， ').join(answer_choices)}}?" }, { "category_id": 15, "poly": [ 303.0, 1738.0, 1182.0, 1738.0, 1182.0, 1774.0, 303.0, 1774.0 ], "score": 0.982, "text": "definition Ill description Ill manner of action Ill reason" }, { "category_id": 15, "poly": [ 306.0, 1932.0, 430.0, 1932.0, 430.0, 1965.0, 306.0, 1965.0 ], "score": 0.998, "text": "{{text}}" }, { "category_id": 15, "poly": [ 301.0, 680.0, 1236.0, 680.0, 1236.0, 712.0, 301.0, 712.0 ], "score": 0.988, "text": "Which category best describes the following question: {{text}}" }, { "category_id": 15, "poly": [ 294.0, 1677.0, 575.0, 1677.0, 575.0, 1717.0, 294.0, 1717.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 303.0, 733.0, 774.0, 733.0, 774.0, 766.0, 303.0, 766.0 ], "score": 1.0, "text": "Choose from the following list:" }, { "category_id": 15, "poly": [ 390.0, 760.0, 744.0, 760.0, 744.0, 797.0, 390.0, 797.0 ], "score": 0.983, "text": ".join(answer_choices)})" }, { "category_id": 15, "poly": [ 294.0, 1008.0, 575.0, 1008.0, 575.0, 1048.0, 294.0, 1048.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 424.0, 575.0, 424.0, 575.0, 464.0, 294.0, 464.0 ], "score": 0.975, "text": "Ans wer Choices Template:" }, { "category_id": 15, "poly": [ 294.0, 620.0, 463.0, 620.0, 463.0, 656.0, 294.0, 656.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1873.0, 463.0, 1873.0, 463.0, 1909.0, 294.0, 1909.0 ], "score": 0.999, "text": "Input Template:" }, { "category_id": 15, "poly": [ 294.0, 1513.0, 475.0, 1513.0, 475.0, 1551.0, 294.0, 1551.0 ], "score": 0.991, "text": "Target Template:" }, { "category_id": 15, "poly": [ 294.0, 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304.0, 783.0 ], "score": 0.984, "text": "{% if label_coarse" }, { "category_id": 15, "poly": [ 655.0, 746.0, 703.0, 746.0, 703.0, 783.0, 655.0, 783.0 ], "score": 0.991, "text": "}" }, { "category_id": 15, "poly": [ 302.0, 775.0, 1224.0, 775.0, 1224.0, 813.0, 302.0, 813.0 ], "score": 0.981, "text": "Is this question asking for an {{'，'.join(answer_choices)}}?" }, { "category_id": 15, "poly": [ 305.0, 805.0, 429.0, 805.0, 429.0, 838.0, 305.0, 838.0 ], "score": 0.997, "text": "{{text}}" }, { "category_id": 15, "poly": [ 294.0, 627.0, 933.0, 627.0, 933.0, 665.0, 294.0, 665.0 ], "score": 0.982, "text": "Prompt not for the original task intended by the dataset authors" }, { "category_id": 15, "poly": [ 295.0, 663.0, 462.0, 663.0, 462.0, 695.0, 295.0, 695.0 ], "score": 0.998, "text": "Input Template:" }, { "category_id": 15, "poly": [ 304.0, 1663.0, 1026.0, 1663.0, 1026.0, 1696.0, 304.0, 1696.0 ], "score": 0.977, "text": "{{answer_choices [label_mapping[label_fine]]}}" }, { "category_id": 15, 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KeyValue
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idx label0 0
sentence1Do you want to come over to my place later?
sentence2A political system with no place for the less prom...
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place
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