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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. 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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. 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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 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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). Other metadata documents" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1365.0, + 1403.0, + 1365.0, + 1403.0, + 1402.0, + 293.0, + 1402.0 + ], + "score": 0.986, + "text": "Lastly, we further evaluate on a subset of the datasets from BIG-bench, which is a recent community-" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1394.0, + 1405.0, + 1394.0, + 1405.0, + 1435.0, + 293.0, + 1435.0 + ], + "score": 0.98, + "text": " driven benchmark to create a diverse collection of diffcult tasks to test the abilities of large language" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1427.0, + 1405.0, + 1427.0, + 1405.0, + 1462.0, + 294.0, + 1462.0 + ], + "score": 0.982, + "text": "models.The subset of BIG-bench comprise a language-oriented selection of tasks for which the BIG-" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1457.0, + 1403.0, + 1457.0, + 1403.0, + 1495.0, + 293.0, + 1495.0 + ], + "score": 0.989, + "text": "bench maintainers have prepared preliminary results and which constitute text that is in-vocabulary" + }, + { + "category_id": 15, + "poly": [ + 292.0, + 1488.0, + 1405.0, + 1488.0, + 1405.0, + 1525.0, + 292.0, + 1525.0 + ], + "score": 0.993, + "text": "for the T5 tokenizer (i.e.only contain English-language text without emojis or other special charac-" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1515.0, + 1191.0, + 1515.0, + 1191.0, + 1558.0, + 293.0, + 1558.0 + ], + "score": 0.988, + "text": " ters). 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. 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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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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. However, the number of examples in each of our training datasets varies by" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1484.0, + 1406.0, + 1484.0, + 1406.0, + 1519.0, + 293.0, + 1519.0 + ], + "score": 0.977, + "text": "two orders of magnitude. We therefore follow the strategy used in Raffel et al. (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). Unless specified otherwise,we report performance" + }, + { + "category_id": 15, + "poly": [ + 295.0, + 1896.0, + 1119.0, + 1896.0, + 1119.0, + 1928.0, + 295.0, + 1928.0 + ], + "score": 0.992, + "text": "on the validation splits.All reported datasets use accuracy as their metric." + }, + { + "category_id": 15, + "poly": [ + 294.0, + 975.0, + 618.0, + 975.0, + 618.0, + 1013.0, + 294.0, + 1013.0 + ], + "score": 0.98, + "text": "Training Our main model," + }, + { + "category_id": 15, + "poly": [ + 652.0, + 975.0, + 1405.0, + 975.0, + 1405.0, + 1013.0, + 652.0, + 1013.0 + ], + "score": 0.99, + "text": ", is trained on the multitask mixture detailed in Section 3 and Table 5." + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1006.0, + 435.0, + 1006.0, + 435.0, + 1042.0, + 294.0, + 1042.0 + ], + "score": 1.0, + "text": "Meanwhile," + }, + { + "category_id": 15, + "poly": [ + 488.0, + 1006.0, + 1405.0, + 1006.0, + 1405.0, + 1042.0, + 488.0, + 1042.0 + ], + "score": 0.982, + "text": " is the same model with identical hyperparameters except trained on a mixture that" + }, + { + "category_id": 15, + "poly": [ + 296.0, + 1039.0, + 767.0, + 1039.0, + 767.0, + 1073.0, + 296.0, + 1073.0 + ], + "score": 0.979, + "text": "adds GPT-3's evaluation datasets. 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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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. 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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). Adding" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 968.0, + 1404.0, + 968.0, + 1404.0, + 1005.0, + 294.0, + 1005.0 + ], + "score": 0.991, + "text": "more training prompts consistently leads to higher median performance and generally lower in-" + }, + { + "category_id": 15, + "poly": [ + 296.0, + 1000.0, + 676.0, + 1000.0, + 676.0, + 1033.0, + 296.0, + 1033.0 + ], + "score": 0.99, + "text": "terquartile range for unseen tasks." + }, + { + "category_id": 15, + "poly": [ + 295.0, + 1195.0, + 634.0, + 1195.0, + 634.0, + 1233.0, + 295.0, + 1233.0 + ], + "score": 0.985, + "text": "6.2PROMPT ROBUSTNESS" + }, + { + "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": [ + 839.0, + 2086.0, + 860.0, + 2086.0, + 860.0, + 2116.0, + 839.0, + 2116.0 + ], + "score": 0.999, + "text": "8" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1384.0, + 1004.0, + 1384.0, + 1004.0, + 1421.0, + 293.0, + 1421.0 + ], + "score": 0.983, + "text": "Effect of More Prompts per DatasetIn this analysis, we fix" + }, + { + "category_id": 15, + "poly": [ + 1024.0, + 1384.0, + 1406.0, + 1384.0, + 1406.0, + 1421.0, + 1024.0, + 1421.0 + ], + "score": 0.994, + "text": "and compare TO to models with a" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1418.0, + 1404.0, + 1418.0, + 1404.0, + 1451.0, + 294.0, + 1451.0 + ], + "score": 0.994, + "text": "varying number of prompts per dataset. 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). We compare" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1506.0, + 533.0, + 1506.0, + 533.0, + 1545.0, + 293.0, + 1545.0 + ], + "score": 0.971, + "text": "T0 to models where" + }, + { + "category_id": 15, + "poly": [ + 614.0, + 1506.0, + 1263.0, + 1506.0, + 1263.0, + 1545.0, + 614.0, + 1545.0 + ], + "score": 0.999, + "text": "(one randomly chosen original-task prompt per dataset)," + }, + { + "category_id": 15, + "poly": [ + 1366.0, + 1506.0, + 1406.0, + 1506.0, + 1406.0, + 1545.0, + 1366.0, + 1545.0 + ], + "score": 0.996, + "text": "on" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1539.0, + 930.0, + 1539.0, + 930.0, + 1573.0, + 293.0, + 1573.0 + ], + "score": 0.99, + "text": "average (all original-tasks prompts for all datasets),and" + }, + { + "category_id": 15, + "poly": [ + 1007.0, + 1539.0, + 1215.0, + 1539.0, + 1215.0, + 1573.0, + 1007.0, + 1573.0 + ], + "score": 0.998, + "text": "(corresponding to" + }, + { + "category_id": 15, + "poly": [ + 1310.0, + 1539.0, + 1406.0, + 1539.0, + 1406.0, + 1573.0, + 1310.0, + 1573.0 + ], + "score": 1.0, + "text": "without" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1570.0, + 1404.0, + 1570.0, + 1404.0, + 1602.0, + 293.0, + 1602.0 + ], + "score": 0.987, + "text": " any prompted training).We train all models with the same hyperparameters and the same number" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 1599.0, + 1404.0, + 1599.0, + 1404.0, + 1635.0, + 293.0, + 1635.0 + ], + "score": 0.99, + "text": "of steps. 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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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. 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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. 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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. 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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
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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
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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
" + }, + { + "category_id": 2, + "poly": [ + 300, + 76, + 814, + 76, + 814, + 104, + 300, + 104 + ], + "score": 0.882 + }, + { + "category_id": 2, + "poly": [ + 834, + 2088, + 865, + 2088, + 865, + 2113, + 834, + 2113 + ], + "score": 0.816 + }, + { + "category_id": 6, + "poly": [ + 297, + 1979, + 1406, + 1979, + 1406, + 2073, + 297, + 2073 + ], + "score": 0.732 + }, + { + "category_id": 7, + "poly": [ + 297, + 1979, + 1406, + 1979, + 1406, + 2073, + 297, + 2073 + ], + "score": 0.134 + }, + { + "category_id": 15, + "poly": [ + 294.0, + 70.0, + 819.0, + 70.0, + 819.0, + 110.0, + 294.0, + 110.0 + ], + "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": "24" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1977.0, + 1404.0, + 1977.0, + 1404.0, + 2014.0, + 294.0, + 2014.0 + ], + "score": 0.987, + "text": "Table 5: Alltraining and evaluation datasets. The dataset are printed in their Hugging Face datasets" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 2008.0, + 1404.0, + 2008.0, + 1404.0, + 2044.0, + 294.0, + 2044.0 + ], + "score": 0.983, + "text": "identifier, where the part after / is their subset name. Hotpot QA is recast as closed-book QA due to" + }, + { + "category_id": 15, + "poly": [ + 292.0, + 2038.0, + 974.0, + 2038.0, + 974.0, + 2074.0, + 292.0, + 2074.0 + ], + "score": 0.992, + "text": "long input length.Full citations are included in Appendix H." + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1977.0, + 1404.0, + 1977.0, + 1404.0, + 2014.0, + 294.0, + 2014.0 + ], + "score": 0.987, + "text": "Table 5: Alltraining and evaluation datasets. The dataset are printed in their Hugging Face datasets" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 2008.0, + 1404.0, + 2008.0, + 1404.0, + 2044.0, + 294.0, + 2044.0 + ], + "score": 0.983, + "text": "identifier, where the part after / is their subset name. Hotpot QA is recast as closed-book QA due to" + }, + { + "category_id": 15, + "poly": [ + 292.0, + 2038.0, + 974.0, + 2038.0, + 974.0, + 2074.0, + 292.0, + 2074.0 + ], + "score": 0.992, + "text": "long input length.Full citations are included in Appendix H." + } + ], + "page_info": { + "page_no": 23, + "width": 1700, + "height": 2200 + } + }, + { + "layout_dets": [ + { + "category_id": 1, + "poly": [ + 298, + 464, + 1404, + 464, + 1404, + 649, + 298, + 649 + ], + "score": 0.978 + }, + { + "category_id": 1, + "poly": [ + 297, + 295, + 1403, + 295, + 1403, + 450, + 297, + 450 + ], + "score": 0.977 + }, + { + "category_id": 1, + "poly": [ + 297, + 979, + 1405, + 979, + 1405, + 1256, + 297, + 1256 + ], + "score": 0.977 + }, + { + "category_id": 5, + "poly": [ + 313, + 781, + 1387, + 781, + 1387, + 949, + 313, + 949 + ], + "score": 0.969, + "html": "
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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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
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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. 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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. 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KeyValue
premiseThe taxpayer met with the accountant to get help f...
hypothesisThe accountant sought help filing taxes.
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KeyValue
answer2
option1Ian
option2Dennis
sentenceIan .volunteered to eat Dennis's menudo
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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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{{optionl}} IIl {{option2}}
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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. 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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, 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}, + { + "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, + 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(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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(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? 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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. 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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， 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}}\"? 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KeyValue
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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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KeyValue
idx 0 label
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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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sentence1 In Paris ，in October 1560 ，he secretly met the E...
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Key Value
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"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": [ + 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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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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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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}}\"? 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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]}
metadata{'split':'train'，'model_in_the_loop':'BiDAF'}
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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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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}}\". Can you tell me what it is?" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 678.0, + 474.0, + 678.0, + 474.0, + 716.0, + 293.0, + 716.0 + ], + "score": 0.998, + "text": "Target Template:" + }, + { + "category_id": 15, + "poly": [ + 301.0, + 571.0, + 417.0, + 571.0, + 417.0, + 603.0, + 301.0, + 603.0 + ], + "score": 1.0, + "text": "Answer:" + }, + { + "category_id": 15, + "poly": [ + 305.0, + 1767.0, + 489.0, + 1767.0, + 489.0, + 1805.0, + 305.0, + 1805.0 + ], + "score": 0.997, + "text": "{{question}}" + }, + { + "category_id": 15, + "poly": [ + 305.0, + 517.0, + 640.0, + 517.0, + 640.0, + 549.0, + 305.0, + 549.0 + ], + "score": 0.99, + "text": "Context: \"{{context}}\"" + }, + { + "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": [ + 308.0, + 243.0, + 682.0, + 243.0, + 682.0, + 274.0, + 308.0, + 274.0 + ], + "score": 0.961, + "text": 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1921.0, + 475.0, + 1955.0, + 305.0, + 1955.0 + ], + "score": 0.924, + "text": "{% endif %}" + }, + { + "category_id": 15, + "poly": [ + 303.0, + 1718.0, + 640.0, + 1718.0, + 640.0, + 1753.0, + 303.0, + 1753.0 + ], + "score": 0.985, + "text": "Question: {{question}}" + }, + { + "category_id": 15, + "poly": [ + 302.0, + 1036.0, + 630.0, + 1036.0, + 630.0, + 1070.0, + 302.0, + 1070.0 + ], + "score": 0.98, + "text": "{% if metadata.split ！" + }, + { + "category_id": 15, + "poly": [ + 658.0, + 1036.0, + 802.0, + 1036.0, + 802.0, + 1070.0, + 658.0, + 1070.0 + ], + "score": 0.974, + "text": "\"test\" %}" + }, + { + "category_id": 15, + "poly": [ + 301.0, + 1065.0, + 1236.0, + 1065.0, + 1236.0, + 1100.0, + 301.0, + 1100.0 + ], + "score": 1.0, + "text": "Extract the answer to the question from the following context." + }, + { + "category_id": 15, + "poly": [ + 303.0, + 1095.0, + 640.0, + 1095.0, + 640.0, + 1126.0, + 303.0, + 1126.0 + ], + "score": 0.98, + "text": "Question: {{question}}" + }, + 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"answer the following question. Note that the answer is present within" + }, + { + "category_id": 15, + "poly": [ + 301.0, + 1660.0, + 445.0, + 1660.0, + 445.0, + 1698.0, + 301.0, + 1698.0 + ], + "score": 0.999, + "text": "the text." + }, + { + "category_id": 15, + "poly": [ + 303.0, + 665.0, + 415.0, + 665.0, + 415.0, + 693.0, + 303.0, + 693.0 + ], + "score": 0.998, + "text": "Answer:" + }, + { + "category_id": 15, + "poly": [ + 304.0, + 1493.0, + 631.0, + 1493.0, + 631.0, + 1532.0, + 304.0, + 1532.0 + ], + "score": 0.961, + "text": "{% if metadata.split ！" + }, + { + "category_id": 15, + "poly": [ + 658.0, + 1493.0, + 805.0, + 1493.0, + 805.0, + 1532.0, + 658.0, + 1532.0 + ], + "score": 0.978, + "text": "\"test\" %}" + }, + { + "category_id": 15, + "poly": [ + 303.0, + 1523.0, + 717.0, + 1523.0, + 717.0, + 1561.0, + 303.0, + 1561.0 + ], + "score": 0.998, + "text": "Given the following passage" + }, + { + "category_id": 15, + "poly": [ + 304.0, + 1579.0, + 518.0, + 1579.0, + 518.0, + 1614.0, + 304.0, + 1614.0 + ], + "score": 0.993, + "text": "\"{{context}}\"," + }, + { + "category_id": 15, + "poly": [ + 305.0, + 607.0, + 640.0, + 607.0, + 640.0, + 640.0, + 305.0, + 640.0 + ], + "score": 0.993, + "text": "Context: \"{{context}}\"" + }, + { + "category_id": 15, + "poly": [ + 307.0, + 324.0, + 683.0, + 324.0, + 683.0, + 355.0, + 307.0, + 355.0 + ], + "score": 0.972, + "text": "{{answers.text | choice}}" + }, + { + "category_id": 15, + "poly": [ + 303.0, + 349.0, + 476.0, + 349.0, + 476.0, + 382.0, + 303.0, + 382.0 + ], + "score": 0.964, + "text": "{% endif %}" + }, + { + "category_id": 15, + "poly": [ + 293.0, + 774.0, + 474.0, + 774.0, + 474.0, + 812.0, + 293.0, + 812.0 + ], + "score": 0.998, + "text": "Target Template:" + }, + { + "category_id": 15, + "poly": [ + 306.0, + 523.0, + 632.0, + 523.0, + 632.0, + 557.0, + 306.0, + 557.0 + ], + "score": 0.96, + "text": "{% if metadata.split !" + }, + { + "category_id": 15, + "poly": [ + 657.0, + 523.0, + 804.0, + 523.0, + 804.0, + 557.0, + 657.0, + 557.0 + ], + "score": 0.941, + "text": "\"test\" %}" + }, + { + "category_id": 15, + "poly": [ + 302.0, + 550.0, + 674.0, + 550.0, + 674.0, + 587.0, + 302.0, + 587.0 + ], + "score": 0.985, + "text": "Question: \"{{question}}\"" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1233.0, + 474.0, + 1233.0, + 474.0, + 1272.0, + 294.0, + 1272.0 + ], + "score": 0.999, + "text": "Target Template:" + }, + { + "category_id": 15, + "poly": [ + 295.0, + 462.0, + 463.0, + 462.0, + 463.0, + 499.0, + 295.0, + 499.0 + ], + "score": 0.972, + "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, + 977.0, + 463.0, + 977.0, + 463.0, + 1013.0, + 295.0, + 1013.0 + ], + "score": 0.999, + "text": "Input Template:" + }, + { + "category_id": 15, + "poly": [ + 295.0, + 1436.0, + 463.0, + 1436.0, + 463.0, + 1472.0, + 295.0, + 1472.0 + ], + "score": 0.999, + "text": "Input Template:" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1831.0, + 475.0, + 1831.0, + 475.0, + 1870.0, + 294.0, + 1870.0 + ], + "score": 0.999, + "text": "Target Template:" + } + ], + "page_info": { + "page_no": 79, + "width": 1700, + "height": 2200 + } + }, + { + "layout_dets": [ + { + "category_id": 5, + "poly": [ + 297, + 438, + 1278, + 438, + 1278, + 667, + 297, + 667 + ], + "score": 0.972, + "html": "
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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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, 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"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
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"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). 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+ "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, + 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{ + "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? 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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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(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, + 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KeyValue
id56be85543aeaaa14008c9063
titleBeyoncé
contextBeyoncé Giselle Knowles-Carter (/bijnse/ bee-Y...
questionWhen did Beyonce start becoming popular?
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KeyValue
answers['Nuria']
entities['Afghanistan'，'Badam Bagh'，'Mariam'，'Nuria']
idx{'passage':0，'query':0}
passageThe harrowing stories of women and children locked...
queryThe baby she gave birth to is her husbands and he ...
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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 ...
id6b819727eb8a670df26a7ffad036c119
question"There are 10 apples on an apple tree. Three fall...
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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
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answer choiceContinue her dancing class.
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answerKeyD
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id question_stem7-980 The sun is responsible for
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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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+ "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. 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"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, + 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"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": 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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")}}
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{% ifanswerKeychoices.label[0] %} Yes 二{% else %} No {% endif %}
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KeyValue
idQuaRel_V1_Fr_0223
answer_index logical_forms1 ['(infer (speed higher worldl） (smoothness higher ...
logical_form_prettyqrel(speed，higher，worldl）-> qrel(smoothness，hi...
world_literals question{'worldl':['ice']，'world2'：['snow']} Mike was snowboarding on the snow and hit a piece
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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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KeyValue
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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]，[
[1，2，3，0]，[1，3，0，[1，0，3，2]， 2]，[1，[1，2，0，3], 3，0]，
[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:
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-{{ answer_choices[order[1]] }}
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answerAlike attending
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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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KeyValue
idx0
label1
passagePersian language -- Persian (/prn，-n/)，al...
questiondo iran and afghanistan speak the same language
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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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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
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metadata_question_typeINPARA_EFFECT
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question_para_step
question_stemsuppose there will be fewer new trees happens，how...
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content This sound track was beautiful! It paints the sene...
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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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Key Value
text labelBromwell High is a cartoon comedy. It ran at the s... 1
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choice2The grass was cut.
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label0
premiseMy body cast a shadow over the grass.
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1 star Ill 2 stars Ill 3 stars IIl 4 stars Ill 5 stars
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Ending 1 IIl Ending 2 IIl Ending 3 IIl Ending 4
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KeyValue
article It's official: U.S. President Barack Obama wants l...
highlights Syrian official: Obama climbed to the top of the t...
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{{highlights}}
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KeyValue
document Recent reports have linked some France-based playe...
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summaryNew Welsh Rugby Union chairman Gareth Davies belie...
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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 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Key Value
text labelWall St. Bears Claw Back Into the Black (Reuters) ... 2
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1158.0, + 1252.0, + 1190.0, + 304.0, + 1190.0 + ], + "score": 0.979, + "text": "{{content}} Given a list of categories: {{\"company， educational" + }, + { + "category_id": 15, + "poly": [ + 303.0, + 1185.0, + 1324.0, + 1185.0, + 1324.0, + 1221.0, + 303.0, + 1221.0 + ], + "score": 0.964, + "text": "institution,artist，athlete,office holder,mean of transportation," + }, + { + "category_id": 15, + "poly": [ + 303.0, + 1214.0, + 1135.0, + 1214.0, + 1135.0, + 1247.0, + 303.0, + 1247.0 + ], + "score": 0.981, + "text": "building，natural place，village，animal，plant，album，" + }, + { + "category_id": 15, + "poly": [ + 1138.0, + 1215.0, + 1373.0, + 1215.0, + 1373.0, + 1245.0, + 1138.0, + 1245.0 + ], + "score": 0.986, + "text": "film or written" + }, + { + "category_id": 15, + "poly": [ + 304.0, + 1242.0, + 1090.0, + 1242.0, + 1090.0, + 1276.0, + 304.0, + 1276.0 + ], + "score": 0.982, + "text": "work\"}}， what category does the paragraph belong to?" + }, + { + "category_id": 15, + "poly": [ + 300.0, 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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, + 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" Description Ill Entity Ill Abbreviation IIl Person IIl Quantity Ill" + }, + { + "category_id": 15, + "poly": [ + 302.0, + 1099.0, + 430.0, + 1099.0, + 430.0, + 1128.0, + 302.0, + 1128.0 + ], + "score": 1.0, + "text": "Location" + }, + { + "category_id": 15, + "poly": [ + 294.0, + 1230.0, + 932.0, + 1230.0, + 932.0, + 1266.0, + 294.0, + 1266.0 + ], + "score": 0.997, + "text": "Prompt not for the original task intended by the dataset authors" + }, + { + "category_id": 15, + "poly": [ + 295.0, + 1264.0, + 462.0, + 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, + 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