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Higher scores are better.", + "additional_details": { + "alphaxiv_y_axis": "security_sales_1 (Accuracy)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Japanese Financial Benchmark - security_sales_1 Securities Broker Test Knowledge" + }, + "metric_id": "japanese_financial_benchmark_security_sales_1_securities_broker_test_knowledge", + "metric_name": "Japanese Financial Benchmark - security_sales_1 Securities Broker Test Knowledge", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 64.91 + }, + "evaluation_result_id": "Japanese Financial Benchmark/Qwen2-57B-A14B-Instruct/1771591481.616601#japanese_financial_benchmark#japanese_financial_benchmark_security_sales_1_securities_broker_test_knowledge" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/11/1b/111b429b-744e-49eb-ae87-8f92f8f4b36a.json b/flat/objects/11/1b/111b429b-744e-49eb-ae87-8f92f8f4b36a.json new file mode 100644 index 0000000000000000000000000000000000000000..e81e35dfb8448aacd082edfa98dce3a2befd9e54 --- /dev/null +++ b/flat/objects/11/1b/111b429b-744e-49eb-ae87-8f92f8f4b36a.json @@ -0,0 +1,381 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "helm_lite/mistralai_mistral-7b-v0.1/1777589798.2391284", + "retrieved_timestamp": "1777589798.2391284", + "source_metadata": { + "source_name": "helm_lite", + "source_type": "documentation", + "source_organization_name": "crfm", + "evaluator_relationship": "third_party" + }, + "eval_library": { + "name": "helm", + "version": "unknown" + }, + "model_info": { + "name": "Mistral v0.1 7B", + "id": "mistralai/mistral-7b-v0.1", + "developer": "mistralai", + "inference_platform": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "Mean win rate", + "source_data": { + "dataset_name": "helm_lite", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "How many models this model outperforms on average (over columns).", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.292, + "details": { + "description": "", + "tab": "Accuracy", + "Mean win rate - Efficiency": "{\"description\": \"\", \"tab\": \"Efficiency\", \"score\": \"0.8075780274656679\"}", + "Mean win rate - General information": "{\"description\": \"\", \"tab\": \"General information\", \"score\": \"\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "NarrativeQA", + "source_data": { + "dataset_name": "NarrativeQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "F1 on NarrativeQA", + "metric_name": "F1", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.716, + "details": { + "description": "min=0.716, mean=0.716, max=0.716, sum=0.716 (1)", + "tab": "Accuracy", + "NarrativeQA - Observed inference time (s)": "{\"description\": \"min=0.705, mean=0.705, max=0.705, sum=0.705 (1)\", \"tab\": \"Efficiency\", \"score\": \"0.7051956902087574\"}", + "NarrativeQA - # eval": "{\"description\": \"min=355, mean=355, max=355, sum=355 (1)\", \"tab\": \"General information\", \"score\": \"355.0\"}", + "NarrativeQA - # train": "{\"description\": \"min=4.575, mean=4.575, max=4.575, sum=4.575 (1)\", \"tab\": \"General information\", \"score\": \"4.574647887323944\"}", + "NarrativeQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "NarrativeQA - # prompt tokens": "{\"description\": \"min=3627.715, mean=3627.715, max=3627.715, sum=3627.715 (1)\", \"tab\": \"General information\", \"score\": \"3627.7154929577464\"}", + "NarrativeQA - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "NaturalQuestions (closed-book)", + "source_data": { + "dataset_name": "NaturalQuestions (closed-book)", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "F1 on NaturalQuestions (closed-book)", + "metric_name": "F1", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.367, + "details": { + "description": "min=0.367, mean=0.367, max=0.367, sum=0.367 (1)", + "tab": "Accuracy", + "NaturalQuestions (open-book) - 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# output tokens": "{\"description\": \"min=0.988, mean=0.988, max=0.988, sum=0.988 (1)\", \"tab\": \"General information\", \"score\": \"0.988\"}", + "NaturalQuestions (closed-book) - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "NaturalQuestions (closed-book) - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "NaturalQuestions (closed-book) - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "NaturalQuestions (closed-book) - # prompt tokens": "{\"description\": \"min=142.069, mean=142.069, max=142.069, sum=142.069 (1)\", \"tab\": \"General information\", \"score\": \"142.069\"}", + "NaturalQuestions (closed-book) - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "mode": "\"closedbook\"" + } + } + }, + { + "evaluation_name": "OpenbookQA", + "source_data": { + "dataset_name": "OpenbookQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on OpenbookQA", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.776, + "details": { + "description": "min=0.776, mean=0.776, max=0.776, sum=0.776 (1)", + "tab": "Accuracy", + "OpenbookQA - 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Observed inference time (s)": "{\"description\": \"min=0.992, mean=1.159, max=1.576, sum=8.114 (7)\", \"tab\": \"Efficiency\", \"score\": \"1.159214100149656\"}", + "MATH - # eval": "{\"description\": \"min=30, mean=62.429, max=135, sum=437 (7)\", \"tab\": \"General information\", \"score\": \"62.42857142857143\"}", + "MATH - # train": "{\"description\": \"min=8, mean=8, max=8, sum=56 (7)\", \"tab\": \"General information\", \"score\": \"8.0\"}", + "MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (7)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MATH - # prompt tokens": "{\"description\": \"min=991.615, mean=1455.266, max=2502.962, sum=10186.865 (7)\", \"tab\": \"General information\", \"score\": \"1455.2664139976257\"}", + "MATH - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=7 (7)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "[\"algebra\", \"counting_and_probability\", \"geometry\", \"intermediate_algebra\", \"number_theory\", \"prealgebra\", \"precalculus\"]", + "level": "\"1\"", + "use_official_examples": "\"False\"", + "use_chain_of_thought": "\"True\"" + } + } + }, + { + "evaluation_name": "GSM8K", + "source_data": { + "dataset_name": "GSM8K", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on GSM8K", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.377, + "details": { + "description": "min=0.377, mean=0.377, max=0.377, sum=0.377 (1)", + "tab": "Accuracy", + "GSM8K - Observed inference time (s)": "{\"description\": \"min=1.632, mean=1.632, max=1.632, sum=1.632 (1)\", \"tab\": \"Efficiency\", \"score\": \"1.6323128745555877\"}", + "GSM8K - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "GSM8K - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "GSM8K - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "GSM8K - # prompt tokens": "{\"description\": \"min=1187.268, mean=1187.268, max=1187.268, sum=1187.268 (1)\", \"tab\": \"General information\", \"score\": \"1187.268\"}", + "GSM8K - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "LegalBench", + "source_data": { + "dataset_name": "LegalBench", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on LegalBench", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.58, + "details": { + "description": "min=0.433, mean=0.58, max=0.789, sum=2.901 (5)", + "tab": "Accuracy", + "LegalBench - Observed inference time (s)": "{\"description\": \"min=0.287, mean=0.353, max=0.577, sum=1.765 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.35307050709631943\"}", + "LegalBench - # eval": "{\"description\": \"min=95, mean=409.4, max=1000, sum=2047 (5)\", \"tab\": \"General information\", \"score\": \"409.4\"}", + "LegalBench - # train": "{\"description\": \"min=1.969, mean=4.194, max=5, sum=20.969 (5)\", \"tab\": \"General information\", \"score\": \"4.1938775510204085\"}", + "LegalBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "LegalBench - # prompt tokens": "{\"description\": \"min=219.453, mean=998.503, max=3534.259, sum=4992.513 (5)\", \"tab\": \"General information\", \"score\": \"998.5025315575822\"}", + "LegalBench - # output tokens": "{\"description\": \"min=0.992, mean=0.998, max=1, sum=4.992 (5)\", \"tab\": \"General information\", \"score\": \"0.9983673469387755\"}" + } + }, + "generation_config": { + "additional_details": { + "subset": "[\"abercrombie\", \"corporate_lobbying\", \"function_of_decision_section\", \"international_citizenship_questions\", \"proa\"]" + } + } + }, + { + "evaluation_name": "MedQA", + "source_data": { + "dataset_name": "MedQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on MedQA", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.525, + "details": { + "description": "min=0.525, mean=0.525, max=0.525, sum=0.525 (1)", + "tab": "Accuracy", + "MedQA - Observed inference time (s)": "{\"description\": \"min=0.348, mean=0.348, max=0.348, sum=0.348 (1)\", \"tab\": \"Efficiency\", \"score\": \"0.3478535307093596\"}", + "MedQA - # eval": "{\"description\": \"min=503, mean=503, max=503, sum=503 (1)\", \"tab\": \"General information\", \"score\": \"503.0\"}", + "MedQA - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "MedQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MedQA - # prompt tokens": "{\"description\": \"min=1193.093, mean=1193.093, max=1193.093, sum=1193.093 (1)\", \"tab\": \"General information\", \"score\": \"1193.0934393638172\"}", + "MedQA - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "WMT 2014", + "source_data": { + "dataset_name": "WMT 2014", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "BLEU-4 on WMT 2014", + "metric_name": "BLEU-4", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.16, + "details": { + "description": "min=0.056, mean=0.16, max=0.201, sum=0.802 (5)", + "tab": "Accuracy", + "WMT 2014 - 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This is the paper's primary benchmark for evaluating true abstract relational reasoning. 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This tests whether explicit rule supervision improves reasoning. 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This metric measures how successfully a model's textual knowledge has been updated after an edit, based on questions that can be answered without the image. Higher scores indicate better performance. Scores represent the percentage of correct responses.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MMKE-Bench: Average Text Reliability (T-Rel)" + }, + "metric_id": "mmke_bench_average_text_reliability_t_rel", + "metric_name": "MMKE-Bench: Average Text Reliability (T-Rel)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 26.68 + }, + "evaluation_result_id": "MMKE-Bench/MiniGPT-4 (SERAC)/1771591481.616601#mmke_bench#mmke_bench_average_text_reliability_t_rel" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/11/d6/11d67692-3e8a-42b5-9ce0-ad10cfa7ed82.json b/flat/objects/11/d6/11d67692-3e8a-42b5-9ce0-ad10cfa7ed82.json new file mode 100644 index 0000000000000000000000000000000000000000..5f77868f0c94b01376055f4e1c1e3f2d033e7929 --- /dev/null +++ b/flat/objects/11/d6/11d67692-3e8a-42b5-9ce0-ad10cfa7ed82.json @@ -0,0 +1,118 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "AgentCoMa/GeneralReasoner 7B/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "Imperial College London", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "GeneralReasoner 7B", + "name": "GeneralReasoner 7B", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "AgentCoMa", + "source_data": { + "dataset_name": "AgentCoMa", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2508.19988" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the AgentCoMa test set for compositional questions. 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This task comprehensively assesses medical comprehension, reasoning, and problem-solving. Responses are evaluated by GPT-4o as an examiner. The maximum possible score is 10.", + "additional_details": { + "alphaxiv_y_axis": "CA Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "PediaBench Case Analysis (CA) Performance" + }, + "metric_id": "pediabench_case_analysis_ca_performance", + "metric_name": "PediaBench Case Analysis (CA) Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 2.24 + }, + "evaluation_result_id": "PediaBench/BianQue-7B/1771591481.616601#pediabench#pediabench_case_analysis_ca_performance" + }, + { + "evaluation_name": "PediaBench", + "source_data": { + "dataset_name": "PediaBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.06287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance score on Essay/Short Answer (ES) questions from PediaBench. This subjective task is evaluated by GPT-4o as an examiner, assessing the ability to generate coherent and accurate medical text. The maximum possible score is 30.", + "additional_details": { + "alphaxiv_y_axis": "ES Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "PediaBench Essay/Short Answer (ES) Performance" + }, + "metric_id": "pediabench_essay_short_answer_es_performance", + "metric_name": "PediaBench Essay/Short Answer (ES) Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 13.01 + }, + "evaluation_result_id": "PediaBench/BianQue-7B/1771591481.616601#pediabench#pediabench_essay_short_answer_es_performance" + }, + { + "evaluation_name": "PediaBench", + "source_data": { + "dataset_name": "PediaBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.06287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance score on Multiple Choice (MC) questions from the PediaBench dataset. This task evaluates an LLM's capacity to distinguish between similar or related medical concepts. The score is weighted by question difficulty, with a maximum possible score of 40.", + "additional_details": { + "alphaxiv_y_axis": "MC Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "PediaBench Multiple Choice (MC) Performance" + }, + "metric_id": "pediabench_multiple_choice_mc_performance", + "metric_name": "PediaBench Multiple Choice (MC) Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0 + }, + "evaluation_result_id": "PediaBench/BianQue-7B/1771591481.616601#pediabench#pediabench_multiple_choice_mc_performance" + }, + { + "evaluation_name": "PediaBench", + "source_data": { + "dataset_name": "PediaBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.06287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance score on Pairing (PA) questions from the PediaBench dataset. This task requires exact matching and assesses fine-grained discrimination among similar concepts. The score is weighted, with a maximum possible score of 10.", + "additional_details": { + "alphaxiv_y_axis": "PA Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "PediaBench Pairing (PA) Performance" + }, + "metric_id": "pediabench_pairing_pa_performance", + "metric_name": "PediaBench Pairing (PA) Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0 + }, + "evaluation_result_id": "PediaBench/BianQue-7B/1771591481.616601#pediabench#pediabench_pairing_pa_performance" + }, + { + "evaluation_name": "PediaBench", + "source_data": { + "dataset_name": "PediaBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.06287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance score on True or False (ToF) questions from the PediaBench dataset. This task tests an LLM's ability to match statements with concepts and facts and reason to detect errors. The score is weighted by question difficulty, with a maximum possible score of 10.", + "additional_details": { + "alphaxiv_y_axis": "ToF Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "PediaBench True or False (ToF) Performance" + }, + "metric_id": "pediabench_true_or_false_tof_performance", + "metric_name": "PediaBench True or False (ToF) Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0 + }, + "evaluation_result_id": "PediaBench/BianQue-7B/1771591481.616601#pediabench#pediabench_true_or_false_tof_performance" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/11/f3/11f36bfe-8494-4932-ab0b-c2b59602582f.json b/flat/objects/11/f3/11f36bfe-8494-4932-ab0b-c2b59602582f.json new file mode 100644 index 0000000000000000000000000000000000000000..fef7ce06427b41f73e11c738bfb328d47093c311 --- /dev/null +++ b/flat/objects/11/f3/11f36bfe-8494-4932-ab0b-c2b59602582f.json 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This metric provides a holistic measure of a model's robustness and reliability in code reasoning when faced with various forms of misleading structural and natural language information.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Average", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "CodeCrash: Average Robustness to Perturbations" + }, + "metric_id": "codecrash_average_robustness_to_perturbations", + "metric_name": "CodeCrash: Average Robustness to Perturbations", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 56.4 + }, + "evaluation_result_id": "CodeCrash/GPT-4o/1771591481.616601#codecrash#codecrash_average_robustness_to_perturbations" + }, + { + "evaluation_name": "CodeCrash", + "source_data": { + "dataset_name": "CodeCrash", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2504.14119" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Pass@1 accuracy under Misleading Code Comments (MCC) perturbation, where comments that contradict the code's logic are inserted. This metric assesses a model's ability to prioritize executable code semantics over distracting and incorrect natural language information.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Misleading Comments (MCC)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CodeCrash: Robustness to Misleading Code Comments (MCC)" + }, + "metric_id": "codecrash_robustness_to_misleading_code_comments_mcc", + "metric_name": "CodeCrash: Robustness to Misleading Code Comments (MCC)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 55.5 + }, + "evaluation_result_id": "CodeCrash/GPT-4o/1771591481.616601#codecrash#codecrash_robustness_to_misleading_code_comments_mcc" + }, + { + "evaluation_name": "CodeCrash", + "source_data": { + "dataset_name": "CodeCrash", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2504.14119" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Pass@1 accuracy under Misleading Hint Comments (MHC) perturbation, where plausible but incorrect high-level hints about the program's output are added as comments. This metric stress-tests a model's critical reasoning and its ability to avoid 'rationalization'—producing faulty logic to align with an incorrect hint.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Misleading Hint Comments (MHC)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CodeCrash: Robustness to Misleading Hint Comments (MHC)" + }, + "metric_id": "codecrash_robustness_to_misleading_hint_comments_mhc", + "metric_name": "CodeCrash: Robustness to Misleading Hint Comments (MHC)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 59.5 + }, + "evaluation_result_id": "CodeCrash/GPT-4o/1771591481.616601#codecrash#codecrash_robustness_to_misleading_hint_comments_mhc" + }, + { + "evaluation_name": "CodeCrash", + "source_data": { + "dataset_name": "CodeCrash", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2504.14119" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Pass@1 accuracy under Misleading Print Statements (MPS) perturbation, which embeds print statements conveying incorrect information about the code's behavior. This metric measures a model's ability to distinguish executable logic from non-functional but misleading textual output within the code.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Misleading Print Statements (MPS)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CodeCrash: Robustness to Misleading Print Statements (MPS)" + }, + "metric_id": "codecrash_robustness_to_misleading_print_statements_mps", + "metric_name": "CodeCrash: Robustness to Misleading Print Statements (MPS)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 55.2 + }, + "evaluation_result_id": "CodeCrash/GPT-4o/1771591481.616601#codecrash#codecrash_robustness_to_misleading_print_statements_mps" + }, + { + "evaluation_name": "CodeCrash", + "source_data": { + "dataset_name": "CodeCrash", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2504.14119" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Pass@1 accuracy under Aggregated Structural Perturbation (PSC-ALL), which combines variable renaming, code reformatting, and garbage code insertion. This metric tests a model's ability to reason about code logic independent of its superficial syntactic structure and formatting.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Structural Perturbation (PSC-ALL)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CodeCrash: Robustness to Structural Perturbations (PSC-ALL)" + }, + "metric_id": "codecrash_robustness_to_structural_perturbations_psc_all", + "metric_name": "CodeCrash: Robustness to Structural Perturbations (PSC-ALL)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 55.2 + }, + "evaluation_result_id": "CodeCrash/GPT-4o/1771591481.616601#codecrash#codecrash_robustness_to_structural_perturbations_psc_all" + }, + { + "evaluation_name": "CodeCrash", + "source_data": { + "dataset_name": "CodeCrash", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2504.14119" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Pass@1 accuracy on the vanilla (unperturbed) version of the CODECRASH benchmark, aggregated over the CRUX and LCB datasets. This score represents the baseline code reasoning capability of each model before being subjected to misleading structural or natural language perturbations.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Vanilla", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CodeCrash: Baseline Performance (Vanilla)" + }, + "metric_id": "codecrash_baseline_performance_vanilla", + "metric_name": "CodeCrash: Baseline Performance (Vanilla)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 68.8 + }, + "evaluation_result_id": "CodeCrash/GPT-4o/1771591481.616601#codecrash#codecrash_baseline_performance_vanilla" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/11/f6/11f6ed76-cb44-4b87-b559-212fade85ed7.json b/flat/objects/11/f6/11f6ed76-cb44-4b87-b559-212fade85ed7.json new file mode 100644 index 0000000000000000000000000000000000000000..c47900554b9a6eab2427afd339bd4cffc8add495 --- 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These questions typically have a 'yes' answer and correspond to edited images that are visually similar to the originals but semantically opposite. 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These questions typically have a 'no' answer and assess a model's ability to correctly identify the true reality in the base visual scenarios, resisting misleading visual cues.", + "additional_details": { + "alphaxiv_y_axis": "No-Acc", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "BLINK-Twice: Accuracy on Main Questions (No-Acc)" + }, + "metric_id": "blink_twice_accuracy_on_main_questions_no_acc", + "metric_name": "BLINK-Twice: Accuracy on Main Questions (No-Acc)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.529 + }, + "evaluation_result_id": "BLINK-Twice/InternVL2-26B/1771591481.616601#blink_twice#blink_twice_accuracy_on_main_questions_no_acc" + }, + { + "evaluation_name": "BLINK-Twice", + "source_data": { + "dataset_name": "BLINK-Twice", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2510.09361" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The Chain-of-Thought (CoT) Score evaluates the quality of a model's reasoning process, not just the final answer. It assesses whether the model's generated reasoning chain correctly identifies detailed visual cues (1 point) and infers the true reality (1 point), with a maximum score of 2 per question, normalized to a [0, 1] range. This metric helps distinguish correct answers due to sound reasoning from those due to guessing.", + "additional_details": { + "alphaxiv_y_axis": "CoT Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "BLINK-Twice: Chain-of-Thought Score (CoT Score)" + }, + "metric_id": "blink_twice_chain_of_thought_score_cot_score", + "metric_name": "BLINK-Twice: Chain-of-Thought Score (CoT Score)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.288 + }, + "evaluation_result_id": "BLINK-Twice/InternVL2-26B/1771591481.616601#blink_twice#blink_twice_chain_of_thought_score_cot_score" + }, + { + "evaluation_name": "BLINK-Twice", + "source_data": { + "dataset_name": "BLINK-Twice", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2510.09361" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Image Accuracy (I-Acc) measures the percentage of images for which a model correctly answers both the main question and the adversarial question. This metric requires a model to understand not only the original visual scene but also its semantically opposite, yet visually similar, counterpart.", + "additional_details": { + "alphaxiv_y_axis": "I-Acc", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "BLINK-Twice: Image Accuracy (I-Acc)" + }, + "metric_id": "blink_twice_image_accuracy_i_acc", + "metric_name": "BLINK-Twice: Image Accuracy (I-Acc)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.188 + }, + "evaluation_result_id": "BLINK-Twice/InternVL2-26B/1771591481.616601#blink_twice#blink_twice_image_accuracy_i_acc" + }, + { + "evaluation_name": "BLINK-Twice", + "source_data": { + "dataset_name": "BLINK-Twice", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2510.09361" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Question Accuracy (Q-Acc) on the BLINK-Twice benchmark measures the percentage of images where at least one of the two associated binary questions (main or adversarial) is answered correctly. This provides a broad measure of a model's ability to engage with the visual content correctly in at least one context.", + "additional_details": { + "alphaxiv_y_axis": "Q-Acc", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "BLINK-Twice: Question Accuracy (Q-Acc)" + }, + "metric_id": "blink_twice_question_accuracy_q_acc", + "metric_name": "BLINK-Twice: Question Accuracy (Q-Acc)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.429 + }, + "evaluation_result_id": "BLINK-Twice/InternVL2-26B/1771591481.616601#blink_twice#blink_twice_question_accuracy_q_acc" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/20/27/20278359-4e66-41c6-88a0-1f899b8714f6.json b/flat/objects/20/27/20278359-4e66-41c6-88a0-1f899b8714f6.json new file mode 100644 index 0000000000000000000000000000000000000000..f9c7986b07d3841c6883599000473a8664423d75 --- /dev/null +++ b/flat/objects/20/27/20278359-4e66-41c6-88a0-1f899b8714f6.json @@ -0,0 +1,208 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "DefAn/GPT-3.5/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "The University of Western Australia", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "GPT-3.5", + "name": "GPT-3.5", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "DefAn", + "source_data": { + "dataset_name": "DefAn", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.09155" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the average percentage of responses that contain factually incorrect information on the DefAn benchmark. 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Performance is measured by Pass@1, the percentage of generated code completions that pass all unit tests on the first attempt.", + "additional_details": { + "alphaxiv_y_axis": "Algorithmic Block Pass@1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "SAFIM Benchmark: Algorithmic Block Completion" + }, + "metric_id": "safim_benchmark_algorithmic_block_completion", + "metric_name": "SAFIM Benchmark: Algorithmic Block Completion", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 23.8 + }, + "evaluation_result_id": "SAFIM/Phi-2/1771591481.616601#safim#safim_benchmark_algorithmic_block_completion" + }, + { + "evaluation_name": "SAFIM", + "source_data": { + "dataset_name": "SAFIM", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.04814" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Tests an LLM’s knowledge of popular APIs and its ability to integrate this knowledge with surrounding code context to deduce correct arguments for masked API calls. Performance is measured by Pass@1, which in this case uses syntactical matching for evaluation as unit tests are impractical due to external dependencies.", + "additional_details": { + "alphaxiv_y_axis": "API Function Call Pass@1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "SAFIM Benchmark: API Function Call Completion" + }, + "metric_id": "safim_benchmark_api_function_call_completion", + "metric_name": "SAFIM Benchmark: API Function Call Completion", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 22.3 + }, + "evaluation_result_id": "SAFIM/Phi-2/1771591481.616601#safim#safim_benchmark_api_function_call_completion" + }, + { + "evaluation_name": "SAFIM", + "source_data": { + "dataset_name": "SAFIM", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.04814" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates an LLM’s understanding of code control flows by requiring it to complete critical conditional expressions within statements like 'for', 'while', and 'if'. Performance is measured by Pass@1, the percentage of generated code completions that pass all unit tests on the first attempt.", + "additional_details": { + "alphaxiv_y_axis": "Control-Flow Pass@1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "SAFIM Benchmark: Control-Flow Completion" + }, + "metric_id": "safim_benchmark_control_flow_completion", + "metric_name": "SAFIM Benchmark: Control-Flow Completion", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 34.8 + }, + "evaluation_result_id": "SAFIM/Phi-2/1771591481.616601#safim#safim_benchmark_control_flow_completion" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/20/9d/209d0b2b-794f-4544-9c20-a3d9aed35723.json b/flat/objects/20/9d/209d0b2b-794f-4544-9c20-a3d9aed35723.json new file mode 100644 index 0000000000000000000000000000000000000000..4b2309ea082d71534da5881e755bd134a3c0150a --- /dev/null +++ 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The metric is PassRate, which measures the relative improvement over a retest baseline, capturing partial correctness.", + "additional_details": { + "alphaxiv_y_axis": "PassRate (%) - Single-Function BugFix", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoreCodeBench: Single-Function Bug Fixing (PassRate)" + }, + "metric_id": "corecodebench_single_function_bug_fixing_passrate", + "metric_name": "CoreCodeBench: Single-Function Bug Fixing (PassRate)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 64.69 + }, + "evaluation_result_id": "CoreCodeBench/Doubao-1.5-pro/1771591481.616601#corecodebench#corecodebench_single_function_bug_fixing_passrate" + }, + { + "evaluation_name": "CoreCodeBench", + "source_data": { + "dataset_name": "CoreCodeBench", + "source_type": "url", + "url": [ + "https://huggingface.co/collections/tubehhh/corecodebench-68256d2faabf4b1610a08caa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the single-function 'Development' task from the CoreCodeBench benchmark. Models are tasked with completing a missing core code segment based on a functional description. The metric is Pass@1, which indicates whether the first solution generated by a model successfully passes all associated unit tests, measuring absolute correctness.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Single-Function Development", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoreCodeBench: Single-Function Code Development (Pass@1)" + }, + "metric_id": "corecodebench_single_function_code_development_pass_1", + "metric_name": "CoreCodeBench: Single-Function Code Development (Pass@1)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 57.7 + }, + "evaluation_result_id": "CoreCodeBench/Doubao-1.5-pro/1771591481.616601#corecodebench#corecodebench_single_function_code_development_pass_1" + }, + { + "evaluation_name": "CoreCodeBench", + "source_data": { + "dataset_name": "CoreCodeBench", + "source_type": "url", + "url": [ + "https://huggingface.co/collections/tubehhh/corecodebench-68256d2faabf4b1610a08caa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the single-function 'Development' task from the CoreCodeBench benchmark. Models are tasked with completing a missing core code segment based on a functional description and surrounding context. The metric is PassRate, which measures the relative improvement over a retest baseline, capturing partial correctness.", + "additional_details": { + "alphaxiv_y_axis": "PassRate (%) - Single-Function Development", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoreCodeBench: Single-Function Code Development (PassRate)" + }, + "metric_id": "corecodebench_single_function_code_development_passrate", + "metric_name": "CoreCodeBench: Single-Function Code Development (PassRate)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 84.22 + }, + "evaluation_result_id": "CoreCodeBench/Doubao-1.5-pro/1771591481.616601#corecodebench#corecodebench_single_function_code_development_passrate" + }, + { + "evaluation_name": "CoreCodeBench", + "source_data": { + "dataset_name": "CoreCodeBench", + "source_type": "url", + "url": [ + "https://huggingface.co/collections/tubehhh/corecodebench-68256d2faabf4b1610a08caa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the single-function 'Test-Driven Development (TDD)' task from the CoreCodeBench benchmark. Models must implement a function's logic based on provided unit tests. The metric is Pass@1, which indicates whether the first solution generated by a model successfully passes all associated unit tests, measuring absolute correctness.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Single-Function TDD", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoreCodeBench: Single-Function Test-Driven Development (Pass@1)" + }, + "metric_id": "corecodebench_single_function_test_driven_development_pass_1", + "metric_name": "CoreCodeBench: Single-Function Test-Driven Development (Pass@1)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 45.5 + }, + "evaluation_result_id": "CoreCodeBench/Doubao-1.5-pro/1771591481.616601#corecodebench#corecodebench_single_function_test_driven_development_pass_1" + }, + { + "evaluation_name": "CoreCodeBench", + "source_data": { + "dataset_name": "CoreCodeBench", + "source_type": "url", + "url": [ + "https://huggingface.co/collections/tubehhh/corecodebench-68256d2faabf4b1610a08caa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the multi-function 'BugFix' task from the CoreCodeBench benchmark. Models must identify and fix bugs that may span multiple related functions. The metric is Pass@1, a strict measure of absolute correctness that highlights the extreme difficulty of this task, with most models unable to achieve a perfect fix.", + "additional_details": { + "alphaxiv_y_axis": "Pass@1 (%) - Multi-Function BugFix", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoreCodeBench: Multi-Function Bug Fixing (Pass@1)" + }, + "metric_id": "corecodebench_multi_function_bug_fixing_pass_1", + "metric_name": "CoreCodeBench: Multi-Function Bug Fixing (Pass@1)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0 + }, + "evaluation_result_id": "CoreCodeBench/Doubao-1.5-pro/1771591481.616601#corecodebench#corecodebench_multi_function_bug_fixing_pass_1" + }, + { + "evaluation_name": "CoreCodeBench", + "source_data": { + "dataset_name": "CoreCodeBench", + "source_type": "url", + "url": [ + "https://huggingface.co/collections/tubehhh/corecodebench-68256d2faabf4b1610a08caa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the single-function 'Test-Driven Development (TDD)' task from the CoreCodeBench benchmark. 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Lower values indicate that a model is better at avoiding making up incorrect facts.", + "additional_details": { + "alphaxiv_y_axis": "Hallucination Rate (H_LM, %)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Overall Hallucination Rate on the Head-to-Tail Benchmark" + }, + "metric_id": "overall_hallucination_rate_on_the_head_to_tail_benchmark", + "metric_name": "Overall Hallucination Rate on the Head-to-Tail Benchmark", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 62.6 + }, + "evaluation_result_id": "Head-to-Tail/Vicuna (13B)/1771591481.616601#head_to_tail#overall_hallucination_rate_on_the_head_to_tail_benchmark" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/20/af/20af5085-318e-4695-a51c-ed3ec3830000.json b/flat/objects/20/af/20af5085-318e-4695-a51c-ed3ec3830000.json new file mode 100644 index 0000000000000000000000000000000000000000..15f8f200d486e02dab88c286fea68965ba757cad --- /dev/null +++ b/flat/objects/20/af/20af5085-318e-4695-a51c-ed3ec3830000.json @@ -0,0 +1,298 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "SNARE/X-VLM/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "South China University of Technology", + "alphaxiv_dataset_type": "image", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "X-VLM", + "name": "X-VLM", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "SNARE", + "source_data": { + "dataset_name": "SNARE", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2308.12898" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the model's ability to correctly identify the sentence describing the relationship between two objects (e.g., 'the girl is wearing the shirt') among sentences with swapped objects or no relationship word. 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It is a sub-factor of the Readability dimension. Results are based on the 'RN Acc. IF' column in Table 5.", + "additional_details": { + "alphaxiv_y_axis": "Readability: Name Convention (RN Acc. IF)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "RACE: Adherence to Naming Conventions" + }, + "metric_id": "race_adherence_to_naming_conventions", + "metric_name": "RACE: Adherence to Naming Conventions", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 51.4 + }, + "evaluation_result_id": "RACE/GPT-3.5-turbo-0125/1771591481.616601#race#race_adherence_to_naming_conventions" + }, + { + "evaluation_name": "RACE", + "source_data": { + "dataset_name": "RACE", + "source_type": "url", + "url": [ + "https://huggingface.co/spaces/jszheng/RACE_leaderboard" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This metric evaluates the readability of generated code by measuring its adherence to user-specified requirements for naming conventions, code length, and comments. The score represents the overall accuracy of producing code that is both functionally correct and follows these readability instructions. Results are based on Table 5 in the paper.", + "additional_details": { + "alphaxiv_y_axis": "Readability Score (R)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "RACE: Code Readability Performance" + }, + "metric_id": "race_code_readability_performance", + "metric_name": "RACE: Code Readability Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 51.4 + }, + "evaluation_result_id": "RACE/GPT-3.5-turbo-0125/1771591481.616601#race#race_code_readability_performance" + }, + { + "evaluation_name": "RACE", + "source_data": { + "dataset_name": "RACE", + "source_type": "url", + "url": [ + "https://huggingface.co/spaces/jszheng/RACE_leaderboard" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This metric, the Normalized Index for Space (NI_S), is a scalar value from 0 to 100 indicating how well the generated code's memory usage meets a specified space complexity requirement. It is a sub-factor of the Efficiency dimension. Results are based on the 'NI_S*' column in Table 5.", + "additional_details": { + "alphaxiv_y_axis": "Efficiency: Space Complexity (NI_S)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "RACE: Space Complexity Performance Score" + }, + "metric_id": "race_space_complexity_performance_score", + "metric_name": "RACE: Space Complexity Performance Score", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 36.5 + }, + "evaluation_result_id": "RACE/GPT-3.5-turbo-0125/1771591481.616601#race#race_space_complexity_performance_score" + }, + { + "evaluation_name": "RACE", + "source_data": { + "dataset_name": "RACE", + "source_type": "url", + "url": [ + "https://huggingface.co/spaces/jszheng/RACE_leaderboard" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This metric measures the percentage of generated code that is both functionally correct and follows specific code length constraints (e.g., max characters per line, max lines per function). It is a sub-factor of the Readability dimension. Results are based on the 'RL Acc. IF' column in Table 5.", + "additional_details": { + "alphaxiv_y_axis": "Readability: Code Length (RL Acc. IF)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "RACE: Adherence to Code Length Constraints" + }, + "metric_id": "race_adherence_to_code_length_constraints", + "metric_name": "RACE: Adherence to Code Length Constraints", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 60.4 + }, + "evaluation_result_id": "RACE/GPT-3.5-turbo-0125/1771591481.616601#race#race_adherence_to_code_length_constraints" + }, + { + "evaluation_name": "RACE", + "source_data": { + "dataset_name": "RACE", + "source_type": "url", + "url": [ + "https://huggingface.co/spaces/jszheng/RACE_leaderboard" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This metric, the Normalized Index for Time (NI_T), is a scalar value from 0 to 100 indicating how well the generated code's runtime performance meets a specified time complexity requirement. It is a sub-factor of the Efficiency dimension. Results are based on the 'NI_T*' column in Table 5.", + "additional_details": { + "alphaxiv_y_axis": "Efficiency: Time Complexity (NI_T)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "RACE: Time Complexity Performance Score" + }, + "metric_id": "race_time_complexity_performance_score", + "metric_name": "RACE: Time Complexity Performance Score", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 27.5 + }, + "evaluation_result_id": "RACE/GPT-3.5-turbo-0125/1771591481.616601#race#race_time_complexity_performance_score" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/27/3b/273b4e10-ae12-42e5-a93f-73071f22bde2.json b/flat/objects/27/3b/273b4e10-ae12-42e5-a93f-73071f22bde2.json new file mode 100644 index 0000000000000000000000000000000000000000..5e9ceb67eb5f42c9e6696eb50823942cc984cf20 --- /dev/null +++ b/flat/objects/27/3b/273b4e10-ae12-42e5-a93f-73071f22bde2.json @@ -0,0 +1,358 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "University of Notre Dame", + "alphaxiv_dataset_type": "image", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "MiniCPM-V2.5", + "name": "MiniCPM-V2.5", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Overall accuracy across all question categories on the MultiChartQA benchmark. This benchmark evaluates a model's ability to understand and reason about information presented across multiple charts. Performance is measured using a combination of exact match for text-based answers and relaxed accuracy for numerical answers.", + "additional_details": { + "alphaxiv_y_axis": "Overall Accuracy (%)", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "Overall Performance on MultiChartQA Benchmark" + }, + "metric_id": "overall_performance_on_multichartqa_benchmark", + "metric_name": "Overall Performance on MultiChartQA Benchmark", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 33.27 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#overall_performance_on_multichartqa_benchmark" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on cross-chart reasoning questions (Parallel, Comparative, Sequential) from the MultiChartQA benchmark when explicit chart references are removed from the questions. This tests the models' ability to infer which chart contains the relevant information for each part of the query.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Cross-Chart Reasoning without Chart References" + }, + "metric_id": "multichartqa_cross_chart_reasoning_without_chart_references", + "metric_name": "MultiChartQA: Cross-Chart Reasoning without Chart References", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 26.28 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_cross_chart_reasoning_without_chart_references" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the 'Comparative Reasoning' category of the MultiChartQA benchmark. This task requires models to analyze and compare information across multiple charts. Performance is an average of Structure and Content sub-categories.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Comparative Reasoning Performance" + }, + "metric_id": "multichartqa_comparative_reasoning_performance", + "metric_name": "MultiChartQA: Comparative Reasoning Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 35.46 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_comparative_reasoning_performance" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the direct question answering subset of MultiChartQA where models are presented with all charts in a set, even though only one is required to answer the question. This tests the model's ability to identify the correct source chart amidst distractors.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Direct QA Performance with All Charts Provided" + }, + "metric_id": "multichartqa_direct_qa_performance_with_all_charts_provided", + "metric_name": "MultiChartQA: Direct QA Performance with All Charts Provided", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 38.21 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_direct_qa_performance_with_all_charts_provided" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the direct question answering subset of MultiChartQA where models are only given the single relevant chart required to answer the question, without any distracting charts. This serves as an upper-bound for single-chart comprehension capability within the dataset.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Direct QA Performance with Only Specified Chart Provided" + }, + "metric_id": "multichartqa_direct_qa_performance_with_only_specified_chart_provided", + "metric_name": "MultiChartQA: Direct QA Performance with Only Specified Chart Provided", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 58.02 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_direct_qa_performance_with_only_specified_chart_provided" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the 'Direct Question' category of the MultiChartQA benchmark. This task evaluates a model's ability to locate a specific chart from a group and extract information from it, as prompted by the question. Performance is an average of Structure and Content sub-categories.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Direct Question Answering Performance" + }, + "metric_id": "multichartqa_direct_question_answering_performance", + "metric_name": "MultiChartQA: Direct Question Answering Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 34.14 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_direct_question_answering_performance" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Overall accuracy on the MultiChartQA benchmark where multiple charts are merged into a single image before being presented to the model. This setup tests the model's ability to reason over visually complex and cluttered inputs without explicit chart separation.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Overall Performance on Merged Charts" + }, + "metric_id": "multichartqa_overall_performance_on_merged_charts", + "metric_name": "MultiChartQA: Overall Performance on Merged Charts", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 25.47 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_overall_performance_on_merged_charts" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Overall accuracy on the MultiChartQA benchmark where models were prompted to answer directly without providing a step-by-step reasoning process (Chain-of-Thought). This tests the models' raw capability to synthesize information from multiple charts.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Overall Performance (No Chain-of-Thought)" + }, + "metric_id": "multichartqa_overall_performance_no_chain_of_thought", + "metric_name": "MultiChartQA: Overall Performance (No Chain-of-Thought)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 30.63 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_overall_performance_no_chain_of_thought" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the 'Parallel Questions' category of the MultiChartQA benchmark. This task assesses a model's ability to locate information across multiple charts and answer independent sub-questions simultaneously. Accuracy is calculated based on the proportion of correctly answered sub-questions.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Parallel Question Answering Performance" + }, + "metric_id": "multichartqa_parallel_question_answering_performance", + "metric_name": "MultiChartQA: Parallel Question Answering Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 32.78 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_parallel_question_answering_performance" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on cross-chart reasoning questions (Parallel, Comparative, Sequential) from the MultiChartQA benchmark when the questions include explicit references to the charts (e.g., 'in the first chart'). This tests the models' ability to use these cues for information retrieval and reasoning across multiple images.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Cross-Chart Reasoning with Chart References" + }, + "metric_id": "multichartqa_cross_chart_reasoning_with_chart_references", + "metric_name": "MultiChartQA: Cross-Chart Reasoning with Chart References", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 27.08 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_cross_chart_reasoning_with_chart_references" + }, + { + "evaluation_name": "MultiChartQA", + "source_data": { + "dataset_name": "MultiChartQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2410.14179" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the 'Sequential Reasoning' category of the MultiChartQA benchmark. This is the most complex task, requiring models to perform multi-step, multi-hop reasoning by tracking and analyzing information about an entity across various charts in a logical sequence.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MultiChartQA: Sequential Reasoning Performance" + }, + "metric_id": "multichartqa_sequential_reasoning_performance", + "metric_name": "MultiChartQA: Sequential Reasoning Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 23.27 + }, + "evaluation_result_id": "MultiChartQA/MiniCPM-V2.5/1771591481.616601#multichartqa#multichartqa_sequential_reasoning_performance" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/27/3b/273b7d6a-d883-4963-9862-7366d7b781f6.json b/flat/objects/27/3b/273b7d6a-d883-4963-9862-7366d7b781f6.json new file mode 100644 index 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This evaluation assesses how instruction-following and safety alignment affect performance on specialized knowledge tasks with few-shot examples.", + "additional_details": { + "alphaxiv_y_axis": "Average Accuracy (%) - 3-shot Chat", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CFinBench Average Accuracy (3-shot, Chat Models)" + }, + "metric_id": "cfinbench_average_accuracy_3_shot_chat_models", + "metric_name": "CFinBench Average Accuracy (3-shot, Chat Models)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 40.85 + }, + "evaluation_result_id": "CFinBench/Baichuan2-7B/1771591481.616601#cfinbench#cfinbench_average_accuracy_3_shot_chat_models" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/27/73/277394f9-4037-4b92-aaaa-a7e2aa392f69.json b/flat/objects/27/73/277394f9-4037-4b92-aaaa-a7e2aa392f69.json new file mode 100644 index 0000000000000000000000000000000000000000..80a83e04dfe068dcd0568785574cecc47cb44de6 --- /dev/null +++ b/flat/objects/27/73/277394f9-4037-4b92-aaaa-a7e2aa392f69.json @@ -0,0 +1,208 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "MASLegalBench/Qwen3-8B/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "Tsinghua University", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Qwen3-8B", + "name": "Qwen3-8B", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "MASLegalBench", + "source_data": { + "dataset_name": "MASLegalBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2509.24922" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of different Meta-LLMs on the MASLegalBench for deductive legal reasoning. 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A higher win rate means the model wins more matchups by being less biased.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (Bias - Gender)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HEIM: Gender Bias Mitigation Win Rate" + }, + "metric_id": "heim_gender_bias_mitigation_win_rate", + "metric_name": "HEIM: Gender Bias Mitigation Win Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.8 + }, + "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/CogView2/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_gender_bias_mitigation_win_rate" + }, + { + "evaluation_name": "Holistic Evaluation of Text-to-Image Models", + "source_data": { + "dataset_name": "Holistic Evaluation of Text-to-Image Models", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2311.04287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures skin tone bias in generated images, specifically the deviation from a uniform distribution across the Monk Skin Tone (MST) scale. 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A higher win rate means the model wins more matchups by being less biased.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (Bias - Skin Tone)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HEIM: Skin Tone Bias Mitigation Win Rate" + }, + "metric_id": "heim_skin_tone_bias_mitigation_win_rate", + "metric_name": "HEIM: Skin Tone Bias Mitigation Win Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.86 + }, + "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/CogView2/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_skin_tone_bias_mitigation_win_rate" + }, + { + "evaluation_name": "Holistic Evaluation of Text-to-Image Models", + "source_data": { + "dataset_name": "Holistic Evaluation of Text-to-Image Models", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2311.04287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the inference speed of the model. The score is a 'win rate,' defined as the probability that the model is faster than another model selected uniformly at random. This is measured by the wall-clock inference runtime on a standardized set of prompts. 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Scenarios include prompts from PartiPrompts (World Knowledge), DrawBench (Reddit Knowledge), and a new dataset of historical figures.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (Knowledge)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HEIM: World Knowledge Win Rate" + }, + "metric_id": "heim_world_knowledge_win_rate", + "metric_name": "HEIM: World Knowledge Win Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0 + }, + "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/CogView2/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_world_knowledge_win_rate" + }, + { + "evaluation_name": "Holistic Evaluation of Text-to-Image Models", + "source_data": { + "dataset_name": "Holistic Evaluation of Text-to-Image Models", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2311.04287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures whether the model generates novel images and avoids copyright infringement (e.g., reproducing watermarks). The score is a 'win rate,' defined as the probability that the model outperforms another model selected uniformly at random in a head-to-head comparison on this aspect. 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This is evaluated using both human-rated photorealism and automated metrics like Fréchet Inception Distance (FID) and Inception Score (IS) on the MS-COCO dataset.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (Quality)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HEIM: Image Quality Win Rate" + }, + "metric_id": "heim_image_quality_win_rate", + "metric_name": "HEIM: Image Quality Win Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.12 + }, + "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/CogView2/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_image_quality_win_rate" + }, + { + "evaluation_name": "Holistic Evaluation of Text-to-Image Models", + "source_data": { + "dataset_name": "Holistic Evaluation of Text-to-Image Models", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2311.04287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the model's ability to understand objects, counts, and spatial relations (compositionality). 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This is evaluated using challenging compositional prompts from datasets like DrawBench, PartiPrompts, and PaintSkills, with correctness assessed by automated object detectors.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (Reasoning)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HEIM: Compositional Reasoning Win Rate" + }, + "metric_id": "heim_compositional_reasoning_win_rate", + "metric_name": "HEIM: Compositional Reasoning Win Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.02 + }, + "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/CogView2/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_compositional_reasoning_win_rate" + }, + { + "evaluation_name": "Holistic Evaluation of Text-to-Image Models", + "source_data": { + "dataset_name": "Holistic Evaluation of Text-to-Image Models", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2311.04287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures how aesthetically pleasing the generated image is. The score is a 'win rate,' defined as the probability that the model outperforms another model selected uniformly at random in a head-to-head comparison on this aspect. Evaluation uses human ratings for overall aesthetics and subject clarity, alongside automated metrics like LAION Aesthetics score and fractal coefficient, across scenarios designed to elicit creative and artistic outputs (e.g., logos, magazine covers).", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (Aesthetics)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HEIM: Aesthetics Win Rate" + }, + "metric_id": "heim_aesthetics_win_rate", + "metric_name": "HEIM: Aesthetics Win Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.553 + }, + "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/CogView2/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_aesthetics_win_rate" + }, + { + "evaluation_name": "Holistic Evaluation of Text-to-Image Models", + "source_data": { + "dataset_name": "Holistic Evaluation of Text-to-Image Models", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2311.04287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the model's propensity to generate toxic or inappropriate images (e.g., violence, sexual content). 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A higher win rate means the model wins more matchups by being safer.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (Toxicity)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HEIM: Toxicity Avoidance Win Rate" + }, + "metric_id": "heim_toxicity_avoidance_win_rate", + "metric_name": "HEIM: Toxicity Avoidance Win Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.48 + }, + "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/CogView2/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_toxicity_avoidance_win_rate" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/4b/a9/4ba92add-1bee-4373-9d55-42962bab5bb4.json b/flat/objects/4b/a9/4ba92add-1bee-4373-9d55-42962bab5bb4.json new file mode 100644 index 0000000000000000000000000000000000000000..a87b6f5f68d07ece6cc80a5ac17da6f8ffad27cf --- /dev/null +++ b/flat/objects/4b/a9/4ba92add-1bee-4373-9d55-42962bab5bb4.json @@ -0,0 +1,148 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "TheAgentCompany/Claude 3.5 Sonnet/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "Carnegie Mellon University", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Claude 3.5 Sonnet", + "name": "Claude 3.5 Sonnet", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "TheAgentCompany", + "source_data": { + "dataset_name": "TheAgentCompany", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.14161" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The average number of steps (LLM calls) taken by the OpenHands agent to execute a task on TheAgentCompany benchmark. 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The task is to perform binary sentiment classification (positive/negative) based on text and a single sampled video frame.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Zero-Shot Accuracy on MOSEI-2" + }, + "metric_id": "zero_shot_accuracy_on_mosei_2", + "metric_name": "Zero-Shot Accuracy on MOSEI-2", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 76.58 + }, + "evaluation_result_id": "MM-InstructEval/LLaVA-v0-13B/1771591481.616601#mm_instructeval#zero_shot_accuracy_on_mosei_2" + }, + { + "evaluation_name": "MM-InstructEval", + "source_data": { + "dataset_name": "MM-InstructEval", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.07229" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates Multimodal Sentiment Analysis (MSA) on the MOSEI-7 dataset, which is derived from video data. The task is to perform sentiment classification across 7 distinct labels based on text and a single sampled video frame.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Zero-Shot Accuracy on MOSEI-7" + }, + "metric_id": "zero_shot_accuracy_on_mosei_7", + "metric_name": "Zero-Shot Accuracy on MOSEI-7", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 28.37 + }, + "evaluation_result_id": "MM-InstructEval/LLaVA-v0-13B/1771591481.616601#mm_instructeval#zero_shot_accuracy_on_mosei_7" + }, + { + "evaluation_name": "MM-InstructEval", + "source_data": { + "dataset_name": "MM-InstructEval", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.07229" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates Multimodal Sentiment Analysis (MSA) on the MOSI-2 dataset, which is derived from video data. The task is to perform binary sentiment classification (positive/negative) based on text and a single sampled video frame.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Zero-Shot Accuracy on MOSI-2" + }, + "metric_id": "zero_shot_accuracy_on_mosi_2", + "metric_name": "Zero-Shot Accuracy on MOSI-2", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 80.18 + }, + "evaluation_result_id": "MM-InstructEval/LLaVA-v0-13B/1771591481.616601#mm_instructeval#zero_shot_accuracy_on_mosi_2" + }, + { + "evaluation_name": "MM-InstructEval", + "source_data": { + "dataset_name": "MM-InstructEval", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.07229" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates Multimodal Sentiment Analysis (MSA) on the MOSI-7 dataset, which is derived from video data. The task is to perform sentiment classification across 7 distinct labels based on text and a single sampled video frame.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Zero-Shot Accuracy on MOSI-7" + }, + "metric_id": "zero_shot_accuracy_on_mosi_7", + "metric_name": "Zero-Shot Accuracy on MOSI-7", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 30.9 + }, + "evaluation_result_id": "MM-InstructEval/LLaVA-v0-13B/1771591481.616601#mm_instructeval#zero_shot_accuracy_on_mosi_7" + }, + { + "evaluation_name": "MM-InstructEval", + "source_data": { + "dataset_name": "MM-InstructEval", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.07229" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates Multimodal Sentiment Analysis (MSA) on the MVSA-Multiple dataset. The task is to detect the overall sentiment (positive, neutral, negative) of a given text-image pair.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Zero-Shot Accuracy on MVSA-Multiple" + }, + "metric_id": "zero_shot_accuracy_on_mvsa_multiple", + "metric_name": "Zero-Shot Accuracy on MVSA-Multiple", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 69.61 + }, + "evaluation_result_id": "MM-InstructEval/LLaVA-v0-13B/1771591481.616601#mm_instructeval#zero_shot_accuracy_on_mvsa_multiple" + }, + { + "evaluation_name": "MM-InstructEval", + "source_data": { + "dataset_name": "MM-InstructEval", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.07229" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates Multimodal Sentiment Analysis (MSA) on the MVSA-Single dataset. 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Observed inference time (s)": "{\"description\": \"min=0.445, mean=0.445, max=0.445, sum=0.89 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.44516249497731525\"}", + "Econometrics - # eval": "{\"description\": \"min=114, mean=114, max=114, sum=228 (2)\", \"tab\": \"General information\", \"score\": \"114.0\"}", + "Econometrics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Econometrics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Econometrics - # prompt tokens": "{\"description\": \"min=613.228, mean=613.228, max=613.228, sum=1226.456 (2)\", \"tab\": \"General information\", \"score\": \"613.2280701754386\"}", + "Econometrics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"econometrics\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_econometrics\"" + } + } + }, + { + "evaluation_name": "Global Facts", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Global Facts", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.69, + "details": { + "description": "min=0.69, mean=0.69, max=0.69, sum=1.38 (2)", + "tab": "Accuracy", + "Global Facts - Observed inference time (s)": "{\"description\": \"min=0.301, mean=0.301, max=0.301, sum=0.602 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3012181663513184\"}", + "Global Facts - # eval": "{\"description\": \"min=100, mean=100, max=100, sum=200 (2)\", \"tab\": \"General information\", \"score\": \"100.0\"}", + "Global Facts - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Global Facts - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Global Facts - # prompt tokens": "{\"description\": \"min=399.69, mean=399.69, max=399.69, sum=799.38 (2)\", \"tab\": \"General information\", \"score\": \"399.69\"}", + "Global Facts - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"global_facts\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_global_facts\"" + } + } + }, + { + "evaluation_name": "Jurisprudence", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Jurisprudence", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.907, + "details": { + "description": "min=0.907, mean=0.907, max=0.907, sum=1.815 (2)", + "tab": "Accuracy", + "Jurisprudence - Observed inference time (s)": "{\"description\": \"min=0.388, mean=0.388, max=0.388, sum=0.776 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3880515495936076\"}", + "Jurisprudence - # eval": "{\"description\": \"min=108, mean=108, max=108, sum=216 (2)\", \"tab\": \"General information\", \"score\": \"108.0\"}", + "Jurisprudence - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Jurisprudence - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Jurisprudence - # prompt tokens": "{\"description\": \"min=391.231, mean=391.231, max=391.231, sum=782.463 (2)\", \"tab\": \"General information\", \"score\": \"391.23148148148147\"}", + "Jurisprudence - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"jurisprudence\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_jurisprudence\"" + } + } + }, + { + "evaluation_name": "Philosophy", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Philosophy", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.894, + "details": { + "description": "min=0.894, mean=0.894, max=0.894, sum=1.788 (2)", + "tab": "Accuracy", + "Philosophy - Observed inference time (s)": "{\"description\": \"min=0.483, mean=0.483, max=0.483, sum=0.965 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.48272855795464714\"}", + "Philosophy - # eval": "{\"description\": \"min=311, mean=311, max=311, sum=622 (2)\", \"tab\": \"General information\", \"score\": \"311.0\"}", + "Philosophy - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Philosophy - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Philosophy - # prompt tokens": "{\"description\": \"min=327.92, mean=327.92, max=327.92, sum=655.839 (2)\", \"tab\": \"General information\", \"score\": \"327.91961414790995\"}", + "Philosophy - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"philosophy\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_philosophy\"" + } + } + }, + { + "evaluation_name": "Professional Psychology", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Professional Psychology", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.899, + "details": { + "description": "min=0.899, mean=0.899, max=0.899, sum=1.797 (2)", + "tab": "Accuracy", + "Professional Medicine - Observed inference time (s)": "{\"description\": \"min=0.448, mean=0.448, max=0.448, sum=0.897 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4483548367724699\"}", + "Professional Accounting - Observed inference time (s)": "{\"description\": \"min=0.419, mean=0.419, max=0.419, sum=0.839 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4192587585313946\"}", + "Professional Law - Observed inference time (s)": "{\"description\": \"min=0.462, mean=0.462, max=0.462, sum=0.924 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.462134175381418\"}", + "Professional Psychology - Observed inference time (s)": "{\"description\": \"min=0.518, mean=0.518, max=0.518, sum=1.036 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5180651210491953\"}", + "Professional Medicine - # eval": "{\"description\": \"min=272, mean=272, max=272, sum=544 (2)\", \"tab\": \"General information\", \"score\": \"272.0\"}", + "Professional Medicine - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Professional Medicine - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Professional Medicine - # prompt tokens": "{\"description\": \"min=1071.18, mean=1071.18, max=1071.18, sum=2142.36 (2)\", \"tab\": \"General information\", \"score\": \"1071.1801470588234\"}", + "Professional Medicine - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "Professional Accounting - # eval": "{\"description\": \"min=282, mean=282, max=282, sum=564 (2)\", \"tab\": \"General information\", \"score\": \"282.0\"}", + "Professional Accounting - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Professional Accounting - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Professional Accounting - # prompt tokens": "{\"description\": \"min=657.206, mean=657.206, max=657.206, sum=1314.411 (2)\", \"tab\": \"General information\", \"score\": \"657.2056737588653\"}", + "Professional Accounting - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "Professional Law - # eval": "{\"description\": \"min=1534, mean=1534, max=1534, sum=3068 (2)\", \"tab\": \"General information\", \"score\": \"1534.0\"}", + "Professional Law - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Professional Law - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Professional Law - # prompt tokens": "{\"description\": \"min=1629.344, mean=1629.344, max=1629.344, sum=3258.687 (2)\", \"tab\": \"General information\", \"score\": \"1629.3435462842242\"}", + "Professional Law - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "Professional Psychology - # eval": "{\"description\": \"min=612, mean=612, max=612, sum=1224 (2)\", \"tab\": \"General information\", \"score\": \"612.0\"}", + "Professional Psychology - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Professional Psychology - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Professional Psychology - # prompt tokens": "{\"description\": \"min=574.518, mean=574.518, max=574.518, sum=1149.036 (2)\", \"tab\": \"General information\", \"score\": \"574.5179738562091\"}", + "Professional Psychology - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"professional_psychology\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_professional_psychology\"" + } + } + }, + { + "evaluation_name": "Us Foreign Policy", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Us Foreign Policy", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.95, + "details": { + "description": "min=0.95, mean=0.95, max=0.95, sum=1.9 (2)", + "tab": "Accuracy", + "Us Foreign Policy - Observed inference time (s)": "{\"description\": \"min=0.512, mean=0.512, max=0.512, sum=1.025 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5122887134552002\"}", + "Us Foreign Policy - # eval": "{\"description\": \"min=100, mean=100, max=100, sum=200 (2)\", \"tab\": \"General information\", \"score\": \"100.0\"}", + "Us Foreign Policy - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Us Foreign Policy - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Us Foreign Policy - # prompt tokens": "{\"description\": \"min=421.71, mean=421.71, max=421.71, sum=843.42 (2)\", \"tab\": \"General information\", \"score\": \"421.71\"}", + "Us Foreign Policy - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"us_foreign_policy\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_us_foreign_policy\"" + } + } + }, + { + "evaluation_name": "Astronomy", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Astronomy", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.947, + "details": { + "description": "min=0.947, mean=0.947, max=0.947, sum=1.895 (2)", + "tab": "Accuracy", + "Astronomy - Observed inference time (s)": "{\"description\": \"min=0.435, mean=0.435, max=0.435, sum=0.869 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4347311226945174\"}", + "Astronomy - # eval": "{\"description\": \"min=152, mean=152, max=152, sum=304 (2)\", \"tab\": \"General information\", \"score\": \"152.0\"}", + "Astronomy - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Astronomy - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Astronomy - # prompt tokens": "{\"description\": \"min=577.349, mean=577.349, max=577.349, sum=1154.697 (2)\", \"tab\": \"General information\", \"score\": \"577.3486842105264\"}", + "Astronomy - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"astronomy\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_astronomy\"" + } + } + }, + { + "evaluation_name": "Business Ethics", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Business Ethics", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.89, + "details": { + "description": "min=0.89, mean=0.89, max=0.89, sum=1.78 (2)", + "tab": "Accuracy", + "Business Ethics - Observed inference time (s)": "{\"description\": \"min=0.52, mean=0.52, max=0.52, sum=1.04 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5199928903579711\"}", + "Business Ethics - # eval": "{\"description\": \"min=100, mean=100, max=100, sum=200 (2)\", \"tab\": \"General information\", \"score\": \"100.0\"}", + "Business Ethics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Business Ethics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Business Ethics - # prompt tokens": "{\"description\": \"min=565.7, mean=565.7, max=565.7, sum=1131.4 (2)\", \"tab\": \"General information\", \"score\": \"565.7\"}", + "Business Ethics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"business_ethics\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_business_ethics\"" + } + } + }, + { + "evaluation_name": "Clinical Knowledge", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Clinical Knowledge", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.894, + "details": { + "description": "min=0.894, mean=0.894, max=0.894, sum=1.789 (2)", + "tab": "Accuracy", + "Clinical Knowledge - Observed inference time (s)": "{\"description\": \"min=0.307, mean=0.307, max=0.307, sum=0.613 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3066561905842907\"}", + "Clinical Knowledge - # eval": "{\"description\": \"min=265, mean=265, max=265, sum=530 (2)\", \"tab\": \"General information\", \"score\": \"265.0\"}", + "Clinical Knowledge - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Clinical Knowledge - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Clinical Knowledge - # prompt tokens": "{\"description\": \"min=400.985, mean=400.985, max=400.985, sum=801.97 (2)\", \"tab\": \"General information\", \"score\": \"400.98490566037736\"}", + "Clinical Knowledge - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"clinical_knowledge\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_clinical_knowledge\"" + } + } + }, + { + "evaluation_name": "Conceptual Physics", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Conceptual Physics", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.923, + "details": { + "description": "min=0.923, mean=0.923, max=0.923, sum=1.847 (2)", + "tab": "Accuracy", + "Conceptual Physics - Observed inference time (s)": "{\"description\": \"min=0.381, mean=0.381, max=0.381, sum=0.763 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3812521427235705\"}", + "Conceptual Physics - # eval": "{\"description\": \"min=235, mean=235, max=235, sum=470 (2)\", \"tab\": \"General information\", \"score\": \"235.0\"}", + "Conceptual Physics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Conceptual Physics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Conceptual Physics - # prompt tokens": "{\"description\": \"min=304.677, mean=304.677, max=304.677, sum=609.353 (2)\", \"tab\": \"General information\", \"score\": \"304.67659574468087\"}", + "Conceptual Physics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"conceptual_physics\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_conceptual_physics\"" + } + } + }, + { + "evaluation_name": "Electrical Engineering", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Electrical Engineering", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.793, + "details": { + "description": "min=0.793, mean=0.793, max=0.793, sum=1.586 (2)", + "tab": "Accuracy", + "Electrical Engineering - Observed inference time (s)": "{\"description\": \"min=0.437, mean=0.437, max=0.437, sum=0.874 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4368692447399271\"}", + "Electrical Engineering - # eval": "{\"description\": \"min=145, mean=145, max=145, sum=290 (2)\", \"tab\": \"General information\", \"score\": \"145.0\"}", + "Electrical Engineering - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Electrical Engineering - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Electrical Engineering - # prompt tokens": "{\"description\": \"min=439.228, mean=439.228, max=439.228, sum=878.455 (2)\", \"tab\": \"General information\", \"score\": \"439.22758620689655\"}", + "Electrical Engineering - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"electrical_engineering\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_electrical_engineering\"" + } + } + }, + { + "evaluation_name": "Elementary Mathematics", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Elementary Mathematics", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.775, + "details": { + "description": "min=0.775, mean=0.775, max=0.775, sum=1.55 (2)", + "tab": "Accuracy", + "Elementary Mathematics - Observed inference time (s)": "{\"description\": \"min=0.374, mean=0.374, max=0.374, sum=0.747 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.37356801449306426\"}", + "Elementary Mathematics - # eval": "{\"description\": \"min=378, mean=378, max=378, sum=756 (2)\", \"tab\": \"General information\", \"score\": \"378.0\"}", + "Elementary Mathematics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Elementary Mathematics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Elementary Mathematics - # prompt tokens": "{\"description\": \"min=532.683, mean=532.683, max=532.683, sum=1065.365 (2)\", \"tab\": \"General information\", \"score\": \"532.6825396825396\"}", + "Elementary Mathematics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"elementary_mathematics\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_elementary_mathematics\"" + } + } + }, + { + "evaluation_name": "Formal Logic", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Formal Logic", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.675, + "details": { + "description": "min=0.675, mean=0.675, max=0.675, sum=1.349 (2)", + "tab": "Accuracy", + "Formal Logic - Observed inference time (s)": "{\"description\": \"min=0.341, mean=0.341, max=0.341, sum=0.683 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3414205180274116\"}", + "Formal Logic - # eval": "{\"description\": \"min=126, mean=126, max=126, sum=252 (2)\", \"tab\": \"General information\", \"score\": \"126.0\"}", + "Formal Logic - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Formal Logic - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Formal Logic - # prompt tokens": "{\"description\": \"min=604.492, mean=604.492, max=604.492, sum=1208.984 (2)\", \"tab\": \"General information\", \"score\": \"604.4920634920635\"}", + "Formal Logic - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"formal_logic\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_formal_logic\"" + } + } + }, + { + "evaluation_name": "High School World History", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on High School World History", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.941, + "details": { + "description": "min=0.941, mean=0.941, max=0.941, sum=1.882 (2)", + "tab": "Accuracy", + "High School Biology - Observed inference time (s)": "{\"description\": \"min=0.511, mean=0.511, max=0.511, sum=1.021 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5105965960410334\"}", + "High School Chemistry - Observed inference time (s)": "{\"description\": \"min=0.338, mean=0.338, max=0.338, sum=0.676 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3379564614131533\"}", + "High School Computer Science - Observed inference time (s)": "{\"description\": \"min=0.397, mean=0.397, max=0.397, sum=0.794 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3969814705848694\"}", + "High School European History - Observed inference time (s)": "{\"description\": \"min=0.594, mean=0.594, max=0.594, sum=1.189 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5944608587207216\"}", + "High School Geography - Observed inference time (s)": "{\"description\": \"min=0.353, mean=0.353, max=0.353, sum=0.706 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3532402262543187\"}", + "High School Government And Politics - Observed inference time (s)": "{\"description\": \"min=0.88, mean=0.88, max=0.88, sum=1.76 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.8798744147305662\"}", + "High School Macroeconomics - Observed inference time (s)": "{\"description\": \"min=0.501, mean=0.501, max=0.501, sum=1.003 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.501340057911017\"}", + "High School Mathematics - Observed inference time (s)": "{\"description\": \"min=0.472, mean=0.472, max=0.472, sum=0.944 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4721549925980745\"}", + "High School Microeconomics - Observed inference time (s)": "{\"description\": \"min=0.406, mean=0.406, max=0.406, sum=0.812 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4058714473948759\"}", + "High School Physics - Observed inference time (s)": "{\"description\": \"min=0.484, mean=0.484, max=0.484, sum=0.968 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.48384577075377205\"}", + "High School Psychology - Observed inference time (s)": "{\"description\": \"min=0.532, mean=0.532, max=0.532, sum=1.063 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5316181160988064\"}", + "High School Statistics - Observed inference time (s)": "{\"description\": \"min=0.518, mean=0.518, max=0.518, sum=1.036 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5179998201352579\"}", + "High School US History - Observed inference time (s)": "{\"description\": \"min=0.573, mean=0.573, max=0.573, sum=1.147 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.5734734535217285\"}", + "High School World History - Observed inference time (s)": "{\"description\": \"min=0.461, mean=0.461, max=0.461, sum=0.923 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4614185592796229\"}", + "High School Biology - # eval": "{\"description\": \"min=310, mean=310, max=310, sum=620 (2)\", \"tab\": \"General information\", \"score\": \"310.0\"}", + "High School Biology - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Biology - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Biology - # prompt tokens": "{\"description\": \"min=504.874, mean=504.874, max=504.874, sum=1009.748 (2)\", \"tab\": \"General information\", \"score\": \"504.8741935483871\"}", + "High School Biology - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Chemistry - # eval": "{\"description\": \"min=203, mean=203, max=203, sum=406 (2)\", \"tab\": \"General information\", \"score\": \"203.0\"}", + "High School Chemistry - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Chemistry - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Chemistry - # prompt tokens": "{\"description\": \"min=495.34, mean=495.34, max=495.34, sum=990.68 (2)\", \"tab\": \"General information\", \"score\": \"495.3399014778325\"}", + "High School Chemistry - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Computer Science - # eval": "{\"description\": \"min=100, mean=100, max=100, sum=200 (2)\", \"tab\": \"General information\", \"score\": \"100.0\"}", + "High School Computer Science - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Computer Science - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Computer Science - # prompt tokens": "{\"description\": \"min=865.8, mean=865.8, max=865.8, sum=1731.6 (2)\", \"tab\": \"General information\", \"score\": \"865.8\"}", + "High School Computer Science - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School European History - # eval": "{\"description\": \"min=165, mean=165, max=165, sum=330 (2)\", \"tab\": \"General information\", \"score\": \"165.0\"}", + "High School European History - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School European History - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School European History - # prompt tokens": "{\"description\": \"min=2793.83, mean=2793.83, max=2793.83, sum=5587.661 (2)\", \"tab\": \"General information\", \"score\": \"2793.830303030303\"}", + "High School European History - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Geography - # eval": "{\"description\": \"min=198, mean=198, max=198, sum=396 (2)\", \"tab\": \"General information\", \"score\": \"198.0\"}", + "High School Geography - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Geography - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Geography - # prompt tokens": "{\"description\": \"min=372.783, mean=372.783, max=372.783, sum=745.566 (2)\", \"tab\": \"General information\", \"score\": \"372.7828282828283\"}", + "High School Geography - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Government And Politics - # eval": "{\"description\": \"min=193, mean=193, max=193, sum=386 (2)\", \"tab\": \"General information\", \"score\": \"193.0\"}", + "High School Government And Politics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Government And Politics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Government And Politics - # prompt tokens": "{\"description\": \"min=463.01, mean=463.01, max=463.01, sum=926.021 (2)\", \"tab\": \"General information\", \"score\": \"463.0103626943005\"}", + "High School Government And Politics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Macroeconomics - # eval": "{\"description\": \"min=390, mean=390, max=390, sum=780 (2)\", \"tab\": \"General information\", \"score\": \"390.0\"}", + "High School Macroeconomics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Macroeconomics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Macroeconomics - # prompt tokens": "{\"description\": \"min=371.451, mean=371.451, max=371.451, sum=742.903 (2)\", \"tab\": \"General information\", \"score\": \"371.4512820512821\"}", + "High School Macroeconomics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Mathematics - # eval": "{\"description\": \"min=270, mean=270, max=270, sum=540 (2)\", \"tab\": \"General information\", \"score\": \"270.0\"}", + "High School Mathematics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Mathematics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Mathematics - # prompt tokens": "{\"description\": \"min=532.456, mean=532.456, max=532.456, sum=1064.911 (2)\", \"tab\": \"General information\", \"score\": \"532.4555555555555\"}", + "High School Mathematics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Microeconomics - # eval": "{\"description\": \"min=238, mean=238, max=238, sum=476 (2)\", \"tab\": \"General information\", \"score\": \"238.0\"}", + "High School Microeconomics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Microeconomics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Microeconomics - # prompt tokens": "{\"description\": \"min=398.739, mean=398.739, max=398.739, sum=797.479 (2)\", \"tab\": \"General information\", \"score\": \"398.73949579831935\"}", + "High School Microeconomics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Physics - # eval": "{\"description\": \"min=151, mean=151, max=151, sum=302 (2)\", \"tab\": \"General information\", \"score\": \"151.0\"}", + "High School Physics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Physics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Physics - # prompt tokens": "{\"description\": \"min=560.238, mean=560.238, max=560.238, sum=1120.477 (2)\", \"tab\": \"General information\", \"score\": \"560.2384105960265\"}", + "High School Physics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Psychology - # eval": "{\"description\": \"min=545, mean=545, max=545, sum=1090 (2)\", \"tab\": \"General information\", \"score\": \"545.0\"}", + "High School Psychology - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Psychology - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Psychology - # prompt tokens": "{\"description\": \"min=492.917, mean=492.917, max=492.917, sum=985.835 (2)\", \"tab\": \"General information\", \"score\": \"492.91743119266056\"}", + "High School Psychology - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School Statistics - # eval": "{\"description\": \"min=216, mean=216, max=216, sum=432 (2)\", \"tab\": \"General information\", \"score\": \"216.0\"}", + "High School Statistics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School Statistics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School Statistics - # prompt tokens": "{\"description\": \"min=787.574, mean=787.574, max=787.574, sum=1575.148 (2)\", \"tab\": \"General information\", \"score\": \"787.574074074074\"}", + "High School Statistics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School US History - # eval": "{\"description\": \"min=204, mean=204, max=204, sum=408 (2)\", \"tab\": \"General information\", \"score\": \"204.0\"}", + "High School US History - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School US History - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School US History - # prompt tokens": "{\"description\": \"min=2220.005, mean=2220.005, max=2220.005, sum=4440.01 (2)\", \"tab\": \"General information\", \"score\": \"2220.0049019607845\"}", + "High School US History - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "High School World History - # eval": "{\"description\": \"min=237, mean=237, max=237, sum=474 (2)\", \"tab\": \"General information\", \"score\": \"237.0\"}", + "High School World History - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "High School World History - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "High School World History - # prompt tokens": "{\"description\": \"min=1424.439, mean=1424.439, max=1424.439, sum=2848.878 (2)\", \"tab\": \"General information\", \"score\": \"1424.4388185654009\"}", + "High School World History - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"high_school_world_history\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_high_school_world_history\"" + } + } + }, + { + "evaluation_name": "Human Sexuality", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Human Sexuality", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.901, + "details": { + "description": "min=0.901, mean=0.901, max=0.901, sum=1.802 (2)", + "tab": "Accuracy", + "Human Aging - Observed inference time (s)": "{\"description\": \"min=0.403, mean=0.403, max=0.403, sum=0.807 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4033327327180871\"}", + "Human Sexuality - Observed inference time (s)": "{\"description\": \"min=0.397, mean=0.397, max=0.397, sum=0.794 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3971163625935562\"}", + "Human Aging - # eval": "{\"description\": \"min=223, mean=223, max=223, sum=446 (2)\", \"tab\": \"General information\", \"score\": \"223.0\"}", + "Human Aging - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Human Aging - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Human Aging - # prompt tokens": "{\"description\": \"min=316.453, mean=316.453, max=316.453, sum=632.906 (2)\", \"tab\": \"General information\", \"score\": \"316.4529147982063\"}", + "Human Aging - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "Human Sexuality - # eval": "{\"description\": \"min=131, mean=131, max=131, sum=262 (2)\", \"tab\": \"General information\", \"score\": \"131.0\"}", + "Human Sexuality - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Human Sexuality - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Human Sexuality - # prompt tokens": "{\"description\": \"min=335.695, mean=335.695, max=335.695, sum=671.389 (2)\", \"tab\": \"General information\", \"score\": \"335.69465648854964\"}", + "Human Sexuality - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"human_sexuality\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_human_sexuality\"" + } + } + }, + { + "evaluation_name": "International Law", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on International Law", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.942, + "details": { + "description": "min=0.942, mean=0.942, max=0.942, sum=1.884 (2)", + "tab": "Accuracy", + "International Law - Observed inference time (s)": "{\"description\": \"min=0.437, mean=0.437, max=0.437, sum=0.875 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4373398063596615\"}", + "International Law - # eval": "{\"description\": \"min=121, mean=121, max=121, sum=242 (2)\", \"tab\": \"General information\", \"score\": \"121.0\"}", + "International Law - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "International Law - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "International Law - # prompt tokens": "{\"description\": \"min=639.504, mean=639.504, max=639.504, sum=1279.008 (2)\", \"tab\": \"General information\", \"score\": \"639.5041322314049\"}", + "International Law - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"international_law\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_international_law\"" + } + } + }, + { + "evaluation_name": "Logical Fallacies", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Logical Fallacies", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.902, + "details": { + "description": "min=0.902, mean=0.902, max=0.902, sum=1.804 (2)", + "tab": "Accuracy", + "Logical Fallacies - Observed inference time (s)": "{\"description\": \"min=0.445, mean=0.445, max=0.445, sum=0.89 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.44485992888000114\"}", + "Logical Fallacies - # eval": "{\"description\": \"min=163, mean=163, max=163, sum=326 (2)\", \"tab\": \"General information\", \"score\": \"163.0\"}", + "Logical Fallacies - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Logical Fallacies - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Logical Fallacies - # prompt tokens": "{\"description\": \"min=445.84, mean=445.84, max=445.84, sum=891.681 (2)\", \"tab\": \"General information\", \"score\": \"445.840490797546\"}", + "Logical Fallacies - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"logical_fallacies\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_logical_fallacies\"" + } + } + }, + { + "evaluation_name": "Machine Learning", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Machine Learning", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.777, + "details": { + "description": "min=0.777, mean=0.777, max=0.777, sum=1.554 (2)", + "tab": "Accuracy", + "Machine Learning - Observed inference time (s)": "{\"description\": \"min=0.414, mean=0.414, max=0.414, sum=0.829 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.41432228897299084\"}", + "Machine Learning - # eval": "{\"description\": \"min=112, mean=112, max=112, sum=224 (2)\", \"tab\": \"General information\", \"score\": \"112.0\"}", + "Machine Learning - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Machine Learning - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Machine Learning - # prompt tokens": "{\"description\": \"min=666.205, mean=666.205, max=666.205, sum=1332.411 (2)\", \"tab\": \"General information\", \"score\": \"666.2053571428571\"}", + "Machine Learning - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"machine_learning\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_machine_learning\"" + } + } + }, + { + "evaluation_name": "Management", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Management", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.913, + "details": { + "description": "min=0.913, mean=0.913, max=0.913, sum=1.825 (2)", + "tab": "Accuracy", + "Management - Observed inference time (s)": "{\"description\": \"min=0.46, mean=0.46, max=0.46, sum=0.92 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4598746878429524\"}", + "Management - # eval": "{\"description\": \"min=103, mean=103, max=103, sum=206 (2)\", \"tab\": \"General information\", \"score\": \"103.0\"}", + "Management - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Management - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Management - # prompt tokens": "{\"description\": \"min=279.485, mean=279.485, max=279.485, sum=558.971 (2)\", \"tab\": \"General information\", \"score\": \"279.4854368932039\"}", + "Management - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"management\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_management\"" + } + } + }, + { + "evaluation_name": "Marketing", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Marketing", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.94, + "details": { + "description": "min=0.94, mean=0.94, max=0.94, sum=1.88 (2)", + "tab": "Accuracy", + "Marketing - Observed inference time (s)": "{\"description\": \"min=0.481, mean=0.481, max=0.481, sum=0.962 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4812224573559231\"}", + "Marketing - # eval": "{\"description\": \"min=234, mean=234, max=234, sum=468 (2)\", \"tab\": \"General information\", \"score\": \"234.0\"}", + "Marketing - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Marketing - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Marketing - # prompt tokens": "{\"description\": \"min=399.85, mean=399.85, max=399.85, sum=799.701 (2)\", \"tab\": \"General information\", \"score\": \"399.85042735042737\"}", + "Marketing - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"marketing\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_marketing\"" + } + } + }, + { + "evaluation_name": "Medical Genetics", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Medical Genetics", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.98, + "details": { + "description": "min=0.98, mean=0.98, max=0.98, sum=1.96 (2)", + "tab": "Accuracy", + "Medical Genetics - Observed inference time (s)": "{\"description\": \"min=0.425, mean=0.425, max=0.425, sum=0.85 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.42490904808044433\"}", + "Medical Genetics - # eval": "{\"description\": \"min=100, mean=100, max=100, sum=200 (2)\", \"tab\": \"General information\", \"score\": \"100.0\"}", + "Medical Genetics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Medical Genetics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Medical Genetics - # prompt tokens": "{\"description\": \"min=343.23, mean=343.23, max=343.23, sum=686.46 (2)\", \"tab\": \"General information\", \"score\": \"343.23\"}", + "Medical Genetics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"medical_genetics\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_medical_genetics\"" + } + } + }, + { + "evaluation_name": "Miscellaneous", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Miscellaneous", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.958, + "details": { + "description": "min=0.958, mean=0.958, max=0.958, sum=1.916 (2)", + "tab": "Accuracy", + "Miscellaneous - Observed inference time (s)": "{\"description\": \"min=0.457, mean=0.457, max=0.457, sum=0.915 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.457414278734385\"}", + "Miscellaneous - # eval": "{\"description\": \"min=783, mean=783, max=783, sum=1566 (2)\", \"tab\": \"General information\", \"score\": \"783.0\"}", + "Miscellaneous - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Miscellaneous - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Miscellaneous - # prompt tokens": "{\"description\": \"min=296.479, mean=296.479, max=296.479, sum=592.958 (2)\", \"tab\": \"General information\", \"score\": \"296.47892720306515\"}", + "Miscellaneous - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"miscellaneous\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_miscellaneous\"" + } + } + }, + { + "evaluation_name": "Moral Scenarios", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Moral Scenarios", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.802, + "details": { + "description": "min=0.802, mean=0.802, max=0.802, sum=1.604 (2)", + "tab": "Accuracy", + "Moral Disputes - Observed inference time (s)": "{\"description\": \"min=0.364, mean=0.364, max=0.364, sum=0.727 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3637407087866282\"}", + "Moral Scenarios - Observed inference time (s)": "{\"description\": \"min=0.462, mean=0.462, max=0.462, sum=0.924 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.46217673823820143\"}", + "Moral Disputes - # eval": "{\"description\": \"min=346, mean=346, max=346, sum=692 (2)\", \"tab\": \"General information\", \"score\": \"346.0\"}", + "Moral Disputes - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Moral Disputes - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Moral Disputes - # prompt tokens": "{\"description\": \"min=474.835, mean=474.835, max=474.835, sum=949.671 (2)\", \"tab\": \"General information\", \"score\": \"474.83526011560696\"}", + "Moral Disputes - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "Moral Scenarios - # eval": "{\"description\": \"min=895, mean=895, max=895, sum=1790 (2)\", \"tab\": \"General information\", \"score\": \"895.0\"}", + "Moral Scenarios - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Moral Scenarios - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Moral Scenarios - # prompt tokens": "{\"description\": \"min=655.068, mean=655.068, max=655.068, sum=1310.136 (2)\", \"tab\": \"General information\", \"score\": \"655.068156424581\"}", + "Moral Scenarios - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"moral_scenarios\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_moral_scenarios\"" + } + } + }, + { + "evaluation_name": "Nutrition", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Nutrition", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.905, + "details": { + "description": "min=0.905, mean=0.905, max=0.905, sum=1.81 (2)", + "tab": "Accuracy", + "Nutrition - Observed inference time (s)": "{\"description\": \"min=0.423, mean=0.423, max=0.423, sum=0.847 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.42327408541261763\"}", + "Nutrition - # eval": "{\"description\": \"min=306, mean=306, max=306, sum=612 (2)\", \"tab\": \"General information\", \"score\": \"306.0\"}", + "Nutrition - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Nutrition - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Nutrition - # prompt tokens": "{\"description\": \"min=581.997, mean=581.997, max=581.997, sum=1163.993 (2)\", \"tab\": \"General information\", \"score\": \"581.9967320261438\"}", + "Nutrition - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"nutrition\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_nutrition\"" + } + } + }, + { + "evaluation_name": "Prehistory", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Prehistory", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.935, + "details": { + "description": "min=0.935, mean=0.935, max=0.935, sum=1.87 (2)", + "tab": "Accuracy", + "Prehistory - Observed inference time (s)": "{\"description\": \"min=0.486, mean=0.486, max=0.486, sum=0.972 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.48604018452726766\"}", + "Prehistory - # eval": "{\"description\": \"min=324, mean=324, max=324, sum=648 (2)\", \"tab\": \"General information\", \"score\": \"324.0\"}", + "Prehistory - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Prehistory - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Prehistory - # prompt tokens": "{\"description\": \"min=513.944, mean=513.944, max=513.944, sum=1027.889 (2)\", \"tab\": \"General information\", \"score\": \"513.9444444444445\"}", + "Prehistory - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"prehistory\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_prehistory\"" + } + } + }, + { + "evaluation_name": "Public Relations", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Public Relations", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.782, + "details": { + "description": "min=0.782, mean=0.782, max=0.782, sum=1.564 (2)", + "tab": "Accuracy", + "Public Relations - Observed inference time (s)": "{\"description\": \"min=0.472, mean=0.472, max=0.472, sum=0.944 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.47211467786268757\"}", + "Public Relations - # eval": "{\"description\": \"min=110, mean=110, max=110, sum=220 (2)\", \"tab\": \"General information\", \"score\": \"110.0\"}", + "Public Relations - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Public Relations - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Public Relations - # prompt tokens": "{\"description\": \"min=402.918, mean=402.918, max=402.918, sum=805.836 (2)\", \"tab\": \"General information\", \"score\": \"402.91818181818184\"}", + "Public Relations - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"public_relations\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_public_relations\"" + } + } + }, + { + "evaluation_name": "Security Studies", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Security Studies", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.833, + "details": { + "description": "min=0.833, mean=0.833, max=0.833, sum=1.665 (2)", + "tab": "Accuracy", + "Security Studies - Observed inference time (s)": "{\"description\": \"min=0.452, mean=0.452, max=0.452, sum=0.905 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.45247335336646255\"}", + "Security Studies - # eval": "{\"description\": \"min=245, mean=245, max=245, sum=490 (2)\", \"tab\": \"General information\", \"score\": \"245.0\"}", + "Security Studies - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Security Studies - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Security Studies - # prompt tokens": "{\"description\": \"min=1166.686, mean=1166.686, max=1166.686, sum=2333.371 (2)\", \"tab\": \"General information\", \"score\": \"1166.6857142857143\"}", + "Security Studies - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"security_studies\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_security_studies\"" + } + } + }, + { + "evaluation_name": "Sociology", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Sociology", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.945, + "details": { + "description": "min=0.945, mean=0.945, max=0.945, sum=1.891 (2)", + "tab": "Accuracy", + "Sociology - Observed inference time (s)": "{\"description\": \"min=0.479, mean=0.479, max=0.479, sum=0.958 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4788183940583794\"}", + "Sociology - # eval": "{\"description\": \"min=201, mean=201, max=201, sum=402 (2)\", \"tab\": \"General information\", \"score\": \"201.0\"}", + "Sociology - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Sociology - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Sociology - # prompt tokens": "{\"description\": \"min=444.269, mean=444.269, max=444.269, sum=888.537 (2)\", \"tab\": \"General information\", \"score\": \"444.2686567164179\"}", + "Sociology - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"sociology\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_sociology\"" + } + } + }, + { + "evaluation_name": "Virology", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on Virology", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.578, + "details": { + "description": "min=0.578, mean=0.578, max=0.578, sum=1.157 (2)", + "tab": "Accuracy", + "Virology - Observed inference time (s)": "{\"description\": \"min=0.473, mean=0.473, max=0.473, sum=0.945 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.47254319794206734\"}", + "Virology - # eval": "{\"description\": \"min=166, mean=166, max=166, sum=332 (2)\", \"tab\": \"General information\", \"score\": \"166.0\"}", + "Virology - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Virology - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Virology - # prompt tokens": "{\"description\": \"min=334.434, mean=334.434, max=334.434, sum=668.867 (2)\", \"tab\": \"General information\", \"score\": \"334.43373493975906\"}", + "Virology - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"virology\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_virology\"" + } + } + }, + { + "evaluation_name": "World Religions", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on World Religions", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.883, + "details": { + "description": "min=0.883, mean=0.883, max=0.883, sum=1.766 (2)", + "tab": "Accuracy", + "World Religions - Observed inference time (s)": "{\"description\": \"min=0.408, mean=0.408, max=0.408, sum=0.815 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4075693944741411\"}", + "World Religions - # eval": "{\"description\": \"min=171, mean=171, max=171, sum=342 (2)\", \"tab\": \"General information\", \"score\": \"171.0\"}", + "World Religions - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "World Religions - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "World Religions - # prompt tokens": "{\"description\": \"min=267.936, mean=267.936, max=267.936, sum=535.871 (2)\", \"tab\": \"General information\", \"score\": \"267.9356725146199\"}", + "World Religions - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"world_religions\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_world_religions\"" + } + } + }, + { + "evaluation_name": "Mean win rate", + "source_data": { + "dataset_name": "helm_mmlu", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/mmlu/benchmark_output/releases/v1.13.0/groups/mmlu_subjects.json" + ] + }, + "metric_config": { + "evaluation_description": "How many models this model outperforms on average (over columns).", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.52, + "details": { + "description": "", + "tab": "Efficiency" + } + }, + "generation_config": { + "additional_details": {} + } + } + ] +} \ No newline at end of file diff --git a/flat/objects/7d/d3/7dd3a497-28a5-4619-88ce-5580703644d0.json b/flat/objects/7d/d3/7dd3a497-28a5-4619-88ce-5580703644d0.json new file mode 100644 index 0000000000000000000000000000000000000000..ff895e132424c901e8d5b1d6e7aa63df79fdf0ab --- /dev/null +++ b/flat/objects/7d/d3/7dd3a497-28a5-4619-88ce-5580703644d0.json @@ -0,0 +1,118 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "ARC AGI 2/samsung/Tiny Recursion Model (TRM)/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "ARC Prize Foundation", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "samsung/Tiny Recursion Model (TRM)", + "name": "Tiny Recursion Model (TRM)", + "developer": "samsung" + }, + "evaluation_results": [ + { + "evaluation_name": "ARC AGI 2", + "source_data": { + "dataset_name": "ARC AGI 2", + "source_type": "url", + "url": [ + "https://arcprize.org/leaderboard" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures a system's ability to demonstrate both high adaptability and high efficiency on the more challenging ARC-AGI-2 benchmark. 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