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This open-ended task requires the model to create a new, similar problem-solution pair based on a seed example, assessing creativity and problem abstraction.", + "additional_details": { + "alphaxiv_y_axis": "Score (1-5)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MathChat: Problem Generation - Instruction Following Score" + }, + "metric_id": "mathchat_problem_generation_instruction_following_score", + "metric_name": "MathChat: Problem Generation - Instruction Following Score", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 4.44 + }, + "evaluation_result_id": "MathChat/Mistral-Instruct/1771591481.616601#mathchat#mathchat_problem_generation_instruction_following_score" + }, + { + "evaluation_name": "MathChat", + "source_data": { + "dataset_name": "MathChat", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.19444" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The Category Average score on the MathChat benchmark, which averages the performance on two main categories: problem-solving (Follow-up QA & Error Correction) and open-ended QA (Error Analysis & Problem Generation). Scores are normalized to a 0-1 scale. Results are for 7B parameter models.", + "additional_details": { + "alphaxiv_y_axis": "Category Average Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MathChat Benchmark: Category Average Score (7B Models)" + }, + "metric_id": "mathchat_benchmark_category_average_score_7b_models", + "metric_name": "MathChat Benchmark: Category Average Score (7B Models)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.507 + }, + "evaluation_result_id": "MathChat/Mistral-Instruct/1771591481.616601#mathchat#mathchat_benchmark_category_average_score_7b_models" + }, + { + "evaluation_name": "MathChat", + "source_data": { + "dataset_name": "MathChat", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.19444" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The Task Average score on the MathChat benchmark, calculated by first averaging the normalized scores within each of the four tasks (Follow-up QA, Error Correction, Error Analysis, Problem Generation) and then averaging those four task scores. Scores are normalized to a 0-1 scale. Results are for 7B parameter models.", + "additional_details": { + "alphaxiv_y_axis": "Task Average Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MathChat Benchmark: Task Average Score (7B Models)" + }, + "metric_id": "mathchat_benchmark_task_average_score_7b_models", + "metric_name": "MathChat Benchmark: Task Average Score (7B Models)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.544 + }, + "evaluation_result_id": "MathChat/Mistral-Instruct/1771591481.616601#mathchat#mathchat_benchmark_task_average_score_7b_models" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/17/26/1726e905-e367-438a-b70e-f7601c54a30a.json b/flat/objects/17/26/1726e905-e367-438a-b70e-f7601c54a30a.json new file mode 100644 index 0000000000000000000000000000000000000000..df521c70bc313eafdd5beadbc4d2b94ff49660fc --- /dev/null +++ b/flat/objects/17/26/1726e905-e367-438a-b70e-f7601c54a30a.json @@ -0,0 +1,118 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "LLM-KG-Bench/Expanding the Vocabulary of BERT/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": "Leipzig University", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Expanding the Vocabulary of BERT", + "name": "Expanding the Vocabulary of BERT", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "LLM-KG-Bench", + "source_data": { + "dataset_name": "LLM-KG-Bench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2308.16622" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the macro-averaged F1-score for predicting all correct object-entities for given subject-entities and relations in a knowledge base. 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This metric from the official leaderboard is designed to differentiate the reasoning capabilities of the most powerful models. The leaderboard includes results from models with both open and limited access. Models marked with '*' were evaluated by the C-Eval team.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "C-Eval Leaderboard: Average Accuracy (Hard Subjects)" + }, + "metric_id": "c_eval_leaderboard_average_accuracy_hard_subjects", + "metric_name": "C-Eval Leaderboard: Average Accuracy (Hard Subjects)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 71.9 + }, + "evaluation_result_id": "C-Eval/Qwen/1771591481.616601#c_eval#c_eval_leaderboard_average_accuracy_hard_subjects" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/17/39/17399261-1f0e-437f-ac9f-0c6168d3319a.json b/flat/objects/17/39/17399261-1f0e-437f-ac9f-0c6168d3319a.json new file mode 100644 index 0000000000000000000000000000000000000000..9c4ee6bfc538de1b6acb617a939aef6eb69d08d8 --- /dev/null +++ b/flat/objects/17/39/17399261-1f0e-437f-ac9f-0c6168d3319a.json @@ -0,0 +1,358 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "MATHCHECK/Gemini-1.5-Pro/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 Macau", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Gemini-1.5-Pro", + "name": "Gemini-1.5-Pro", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "MATHCHECK", + "source_data": { + "dataset_name": "MATHCHECK", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.08733" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Overall performance on the MATHCHECK-GSM benchmark for textual mathematical reasoning. The score is the average across all 16 units of the benchmark, combining 4 task types (Problem Solving, Answerable Judging, Outcome Judging, Process Judging) and 4 problem variants (Original, Problem Understanding, Irrelevant Disturbance, Scenario Understanding). This metric provides a holistic measure of a model's reasoning generalization and robustness.", + "additional_details": { + "alphaxiv_y_axis": "Overall Score (%)", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "MATHCHECK-GSM: Overall Performance" + }, + "metric_id": "mathcheck_gsm_overall_performance", + "metric_name": "MATHCHECK-GSM: Overall Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 86.3 + }, + "evaluation_result_id": "MATHCHECK/Gemini-1.5-Pro/1771591481.616601#mathcheck#mathcheck_gsm_overall_performance" + }, + { + "evaluation_name": "MATHCHECK", + "source_data": { + "dataset_name": "MATHCHECK", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.08733" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Macro-F1 score on the Outcome Judging (OJ) task of the MATHCHECK-GEO benchmark, averaged across four problem variants. This task assesses a model's ability to verify if the final answer of a provided solution to a geometry problem is correct.", + "additional_details": { + "alphaxiv_y_axis": "Macro-F1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MATHCHECK-GEO: Outcome Judging Performance" + }, + "metric_id": "mathcheck_geo_outcome_judging_performance", + "metric_name": "MATHCHECK-GEO: Outcome Judging Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 55 + }, + "evaluation_result_id": "MATHCHECK/Gemini-1.5-Pro/1771591481.616601#mathcheck#mathcheck_geo_outcome_judging_performance" + }, + { + "evaluation_name": "MATHCHECK", + "source_data": { + "dataset_name": "MATHCHECK", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.08733" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Overall performance on the MATHCHECK-GEO benchmark for multi-modal geometry reasoning. The score is the average across all 16 units of the benchmark (4 tasks x 4 variants), evaluating a model's ability to reason over problems combining textual descriptions and visual diagrams. This metric provides a holistic measure of a multi-modal model's geometric reasoning.", + "additional_details": { + "alphaxiv_y_axis": "Overall Score (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MATHCHECK-GEO: Overall Performance" + }, + "metric_id": "mathcheck_geo_overall_performance", + "metric_name": "MATHCHECK-GEO: Overall Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 58.7 + }, + "evaluation_result_id": "MATHCHECK/Gemini-1.5-Pro/1771591481.616601#mathcheck#mathcheck_geo_overall_performance" + }, + { + "evaluation_name": "MATHCHECK", + "source_data": { + "dataset_name": "MATHCHECK", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.08733" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the Problem Solving (PS) task of the MATHCHECK-GEO benchmark, averaged across four problem variants. 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Low performance by specialized math models suggests a lack of this critical reasoning skill.", + "additional_details": { + "alphaxiv_y_axis": "Macro-F1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MATHCHECK-GSM: Answerable Judging Performance" + }, + "metric_id": "mathcheck_gsm_answerable_judging_performance", + "metric_name": "MATHCHECK-GSM: Answerable Judging Performance", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 89.5 + }, + "evaluation_result_id": "MATHCHECK/Gemini-1.5-Pro/1771591481.616601#mathcheck#mathcheck_gsm_answerable_judging_performance" + }, + { + "evaluation_name": "MATHCHECK", + "source_data": { + "dataset_name": "MATHCHECK", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.08733" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on a subset of MATHCHECK-GSM questions (Original Problem - Outcome Judging) that were generated by rules, not rewritten by a GPT model. This evaluation serves as a check to ensure that the LLM-based data generation pipeline does not unfairly bias the benchmark in favor of GPT-family models. The consistent performance ranking with the overall score suggests the bias is acceptable.", + "additional_details": { + "alphaxiv_y_axis": "Score (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MATHCHECK-GSM: Bias Check on Non-GPT-Rewritten Questions" + }, + "metric_id": "mathcheck_gsm_bias_check_on_non_gpt_rewritten_questions", + "metric_name": "MATHCHECK-GSM: Bias Check on Non-GPT-Rewritten Questions", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 84.6 + }, + "evaluation_result_id": "MATHCHECK/Gemini-1.5-Pro/1771591481.616601#mathcheck#mathcheck_gsm_bias_check_on_non_gpt_rewritten_questions" + }, + { + "evaluation_name": "MATHCHECK", + "source_data": { + "dataset_name": "MATHCHECK", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.08733" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Macro-F1 score on the Outcome Judging (OJ) task of the MATHCHECK-GSM benchmark, averaged across four problem variants. 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This is the traditional task where models must derive the correct numerical answer to a math problem. 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Observed inference time (s)": "{\"description\": \"min=1.083, mean=1.516, max=1.771, sum=10.613 (7)\", \"tab\": \"Efficiency\", \"score\": \"1.5161172209789922\"}", + "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=971.652, mean=1438.636, max=2490.962, sum=10070.453 (7)\", \"tab\": \"General information\", \"score\": \"1438.6362030100095\"}", + "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": 0.266, + "details": { + "description": "min=0.266, mean=0.266, max=0.266, sum=0.266 (1)", + "tab": "Accuracy", + "GSM8K - Observed inference time (s)": "{\"description\": \"min=1.737, mean=1.737, max=1.737, sum=1.737 (1)\", \"tab\": \"Efficiency\", \"score\": \"1.7367573575973512\"}", + "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=1207.746, mean=1207.746, max=1207.746, sum=1207.746 (1)\", \"tab\": \"General information\", \"score\": \"1207.746\"}", + "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": 0.591, + "details": { + "description": "min=0.338, mean=0.591, max=0.779, sum=2.955 (5)", + "tab": "Accuracy", + "LegalBench - Observed inference time (s)": "{\"description\": \"min=0.331, mean=0.438, max=0.729, sum=2.189 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.43780977145306127\"}", + "LegalBench - # eval": "{\"description\": \"min=95, mean=409.4, max=1000, sum=2047 (5)\", \"tab\": \"General information\", \"score\": \"409.4\"}", + "LegalBench - # train": "{\"description\": \"min=1.886, mean=4.177, max=5, sum=20.886 (5)\", \"tab\": \"General information\", \"score\": \"4.177142857142857\"}", + "LegalBench - truncated": "{\"description\": \"min=0, mean=0.001, max=0.004, sum=0.004 (5)\", \"tab\": \"General information\", \"score\": \"0.0008163265306122449\"}", + "LegalBench - # prompt tokens": "{\"description\": \"min=222.137, mean=1027.35, max=3642.378, sum=5136.751 (5)\", \"tab\": \"General information\", \"score\": \"1027.3502076083553\"}", + "LegalBench - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "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": 0.392, + "details": { + "description": "min=0.392, mean=0.392, max=0.392, sum=0.392 (1)", + "tab": "Accuracy", + "MedQA - 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Observed inference time (s)": "{\"description\": \"min=0.557, mean=0.691, max=0.814, sum=3.456 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.6911807014709866\"}", + "WMT 2014 - # eval": "{\"description\": \"min=503, mean=568.8, max=832, sum=2844 (5)\", \"tab\": \"General information\", \"score\": \"568.8\"}", + "WMT 2014 - # train": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "WMT 2014 - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "WMT 2014 - # prompt tokens": "{\"description\": \"min=127.523, mean=142.288, max=164.972, sum=711.438 (5)\", \"tab\": \"General information\", \"score\": \"142.28751290334915\"}", + "WMT 2014 - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "language_pair": "[\"cs-en\", \"de-en\", \"fr-en\", \"hi-en\", \"ru-en\"]" + } + } + } + ] +} \ No newline at end of file diff --git a/flat/objects/17/88/178860ec-5891-4cd8-a5d1-3a8dc5415b5f.json b/flat/objects/17/88/178860ec-5891-4cd8-a5d1-3a8dc5415b5f.json new file mode 100644 index 0000000000000000000000000000000000000000..7a5bf0693ce8d1ed7c6cd607ef2b73756c208f4c --- /dev/null +++ b/flat/objects/17/88/178860ec-5891-4cd8-a5d1-3a8dc5415b5f.json @@ -0,0 +1,148 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "360VOTS/SiamX-360/1771591481.616601", + "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": "HKUST", + "alphaxiv_dataset_type": "image", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "SiamX-360", + "name": "SiamX-360", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "360VOTS", + "source_data": { + "dataset_name": "360VOTS", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2404.13953" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Comparison of dedicated VOT trackers against VOS trackers adapted for the VOT task on the 360VOT BBox benchmark. 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This evaluation highlights that VOS models, especially when retrained (XMem*) and combined with the 360 framework (XMem-360*), can outperform specialized VOT trackers.", + "additional_details": { + "alphaxiv_y_axis": "S_dual (AUC) - Cross-Domain", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "Cross-Domain Performance on 360VOT BBox (S_dual AUC)" + }, + "metric_id": "cross_domain_performance_on_360vot_bbox_s_dual_auc", + "metric_name": "Cross-Domain Performance on 360VOT BBox (S_dual AUC)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.391 + }, + "evaluation_result_id": "360VOTS/SiamX-360/1771591481.616601#360vots#cross_domain_performance_on_360vot_bbox_s_dual_auc" + }, + { + "evaluation_name": "360VOTS", + "source_data": { + "dataset_name": "360VOTS", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2404.13953" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Comparison of dedicated VOT trackers against VOS trackers adapted for the VOT task on the 360VOT benchmark, using Bounding Field-of-View (BFoV) annotations. Results are measured using Spherical Success (S_sphere AUC), which computes IoU on the spherical surface. This evaluation tests performance using a more geometrically appropriate representation for omnidirectional video.", + "additional_details": { + "alphaxiv_y_axis": "S_sphere (AUC) - Cross-Domain", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Cross-Domain Performance on 360VOT BFoV (S_sphere AUC)" + }, + "metric_id": "cross_domain_performance_on_360vot_bfov_s_sphere_auc", + "metric_name": "Cross-Domain Performance on 360VOT BFoV (S_sphere AUC)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.262 + }, + "evaluation_result_id": "360VOTS/SiamX-360/1771591481.616601#360vots#cross_domain_performance_on_360vot_bfov_s_sphere_auc" + }, + { + "evaluation_name": "360VOTS", + "source_data": { + "dataset_name": "360VOTS", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2404.13953" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Overall tracking performance of 24 state-of-the-art VOT models on the 360VOT BBox benchmark. The metric is Angle Precision (P_angle), which measures the angular distance between the predicted and ground truth centers in the spherical coordinate system. Scores represent the precision rate at a threshold of 3 degrees.", + "additional_details": { + "alphaxiv_y_axis": "P_angle", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Angle Precision on 360VOT BBox" + }, + "metric_id": "angle_precision_on_360vot_bbox", + "metric_name": "Angle Precision on 360VOT BBox", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.425 + }, + "evaluation_result_id": "360VOTS/SiamX-360/1771591481.616601#360vots#angle_precision_on_360vot_bbox" + }, + { + "evaluation_name": "360VOTS", + "source_data": { + "dataset_name": "360VOTS", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2404.13953" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Overall tracking performance of 24 state-of-the-art Visual Object Tracking (VOT) models and two adapted baselines on the 360VOT benchmark, using standard Bounding Box (BBox) annotations. The metric is Dual Success (S_dual) measured by Area Under Curve (AUC), which accounts for objects crossing the image border in 360-degree videos.", + "additional_details": { + "alphaxiv_y_axis": "S_dual (AUC)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Dual Success (AUC) on 360VOT BBox" + }, + "metric_id": "dual_success_auc_on_360vot_bbox", + "metric_name": "Dual Success (AUC) on 360VOT BBox", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.391 + }, + "evaluation_result_id": "360VOTS/SiamX-360/1771591481.616601#360vots#dual_success_auc_on_360vot_bbox" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/17/8a/178a93d4-4d3e-49e8-a180-dab0f4980959.json b/flat/objects/17/8a/178a93d4-4d3e-49e8-a180-dab0f4980959.json new file mode 100644 index 0000000000000000000000000000000000000000..06171aba6c900e464fa843dd165c6ecccaa8d0e7 --- /dev/null +++ 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+ "source_data": { + "dataset_name": "IFEval", + "source_type": "hf_dataset", + "hf_repo": "google/IFEval" + }, + "metric_config": { + "evaluation_description": "Accuracy on IFEval", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0, + "metric_id": "accuracy", + "metric_name": "Accuracy", + "metric_kind": "accuracy", + "metric_unit": "proportion" + }, + "score_details": { + "score": 0.6382 + }, + "evaluation_result_id": "hfopenllm_v2/SicariusSicariiStuff_Impish_QWEN_7B-1M/1773936498.240187#ifeval#accuracy" + }, + { + "evaluation_name": "BBH", + "source_data": { + "dataset_name": "BBH", + "source_type": "hf_dataset", + "hf_repo": "SaylorTwift/bbh" + }, + "metric_config": { + "evaluation_description": "Accuracy on BBH", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0, + "metric_id": "accuracy", + "metric_name": "Accuracy", + "metric_kind": "accuracy", + "metric_unit": "proportion" + }, + 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The metric is count-all@n=20, which measures the total number of problems an LLM successfully solves by generating 20 test case solutions per problem.", + "additional_details": { + "alphaxiv_y_axis": "CTG count-all@n=20", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderUJB: Code-based Test Generation (count-all@n=20)" + }, + "metric_id": "coderujb_code_based_test_generation_count_all_n_20", + "metric_name": "CoderUJB: Code-based Test Generation (count-all@n=20)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 52 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#coderujb_code_based_test_generation_count_all_n_20" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Code-based Test Generation (CTG) task from the CoderUJB benchmark. This task requires models to read program logic and generate test cases to verify core functionality. The metric is pass-all@k=1, the percentage of problems for which at least one correct test case is generated in a single attempt.", + "additional_details": { + "alphaxiv_y_axis": "CTG pass-all@k=1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderUJB: Code-based Test Generation (pass-all@k=1)" + }, + "metric_id": "coderujb_code_based_test_generation_pass_all_k_1", + "metric_name": "CoderUJB: Code-based Test Generation (pass-all@k=1)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 12.14 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#coderujb_code_based_test_generation_pass_all_k_1" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Defect Detection (DD) task from the CoderUJB benchmark. This is a classification task where models must determine if a given function contains defects. The metric is standard classification accuracy. The paper notes this task is particularly challenging for current LLMs, with most performing near random chance.", + "additional_details": { + "alphaxiv_y_axis": "DD Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderUJB: Defect Detection (Accuracy)" + }, + "metric_id": "coderujb_defect_detection_accuracy", + "metric_name": "CoderUJB: Defect Detection (Accuracy)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 50.32 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#coderujb_defect_detection_accuracy" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Functional Code Generation (FCG) task from the CoderUJB benchmark. The metric is count-all@n=20, which measures the total number of problems an LLM successfully solves by generating 20 solutions per problem. A problem is considered solved if at least one of the 20 solutions is correct.", + "additional_details": { + "alphaxiv_y_axis": "FCG count-all@n=20", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderUJB: Functional Code Generation (count-all@n=20)" + }, + "metric_id": "coderujb_functional_code_generation_count_all_n_20", + "metric_name": "CoderUJB: Functional Code Generation (count-all@n=20)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 75 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#coderujb_functional_code_generation_count_all_n_20" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Functional Code Generation (FCG) task from the CoderUJB benchmark. This task requires models to generate function code based on provided annotations, context, and signatures. The metric is pass-all@k=1, which measures the percentage of problems for which at least one correct solution is generated in a single attempt (k=1 from n=20 samples).", + "additional_details": { + "alphaxiv_y_axis": "FCG pass-all@k=1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderUJB: Functional Code Generation (pass-all@k=1)" + }, + "metric_id": "coderujb_functional_code_generation_pass_all_k_1", + "metric_name": "CoderUJB: Functional Code Generation (pass-all@k=1)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 15.32 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#coderujb_functional_code_generation_pass_all_k_1" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Issue-based Test Generation (ITG) task from the CoderUJB benchmark. The metric is count-all@n=20, which measures the total number of issues for which an LLM successfully generates a bug-reproducing test case within 20 attempts.", + "additional_details": { + "alphaxiv_y_axis": "ITG count-all@n=20", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderUJB: Issue-based Test Generation (count-all@n=20)" + }, + "metric_id": "coderujb_issue_based_test_generation_count_all_n_20", + "metric_name": "CoderUJB: Issue-based Test Generation (count-all@n=20)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 64 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#coderujb_issue_based_test_generation_count_all_n_20" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Issue-based Test Generation (ITG) task from the CoderUJB benchmark. Models analyze an issue report and generate a test case to reproduce the bug. The metric is pass-all@k=1, the percentage of problems for which at least one correct bug-reproducing test case is generated in a single attempt.", + "additional_details": { + "alphaxiv_y_axis": "ITG pass-all@k=1 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderUJB: Issue-based Test Generation (pass-all@k=1)" + }, + "metric_id": "coderujb_issue_based_test_generation_pass_all_k_1", + "metric_name": "CoderUJB: Issue-based Test Generation (pass-all@k=1)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 6.32 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#coderujb_issue_based_test_generation_pass_all_k_1" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Model performance on the Java subset of the HumanEval benchmark, measured by pass-all@k=1. HumanEval is a standard benchmark for evaluating functional code generation for standalone functions. These results are included to contrast with the more complex, project-based CoderUJB benchmark.", + "additional_details": { + "alphaxiv_y_axis": "HumanEval pass-all@k=1 (Java) (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HumanEval (Java) pass-all@k=1" + }, + "metric_id": "humaneval_java_pass_all_k_1", + "metric_name": "HumanEval (Java) pass-all@k=1", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 28.53 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#humaneval_java_pass_all_k_1" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Model performance on the Java subset of the CoderEval benchmark, measured by pass-all@k=1. CoderEval tests the ability to generate code that passes provided unit tests. These results are included as a point of comparison against the project-level evaluation in CoderUJB.", + "additional_details": { + "alphaxiv_y_axis": "CoderEval pass-all@k=1 (Java) (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CoderEval (Java) pass-all@k=1" + }, + "metric_id": "codereval_java_pass_all_k_1", + "metric_name": "CoderEval (Java) pass-all@k=1", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 30.58 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#codereval_java_pass_all_k_1" + }, + { + "evaluation_name": "CoderUJB", + "source_data": { + "dataset_name": "CoderUJB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2403.19287" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Model performance on the original Python version of the HumanEval benchmark, measured by pass-all@k=1. This is a widely used metric for code generation capability. These results serve as a baseline to demonstrate the relative difficulty of CoderUJB.", + "additional_details": { + "alphaxiv_y_axis": "HumanEval pass-all@k=1 (Python) (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "HumanEval (Python) pass-all@k=1" + }, + "metric_id": "humaneval_python_pass_all_k_1", + "metric_name": "HumanEval (Python) pass-all@k=1", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 30.35 + }, + "evaluation_result_id": "CoderUJB/StarCoderBase-15B/1771591481.616601#coderujb#humaneval_python_pass_all_k_1" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/1a/45/1a458ad0-8daf-4e1b-9323-eb09f1b290a8.json b/flat/objects/1a/45/1a458ad0-8daf-4e1b-9323-eb09f1b290a8.json new file mode 100644 index 0000000000000000000000000000000000000000..e95aa6308ee3e6302cf83425a640d1baf252c806 --- /dev/null +++ b/flat/objects/1a/45/1a458ad0-8daf-4e1b-9323-eb09f1b290a8.json @@ -0,0 +1,88 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "KnowShiftQA/SPLADE/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 Tokyo", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "SPLADE", + "name": "SPLADE", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "KnowShiftQA", + "source_data": { + "dataset_name": "KnowShiftQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.08985" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Recall@1 (R@1) measures the percentage of queries for which the correct document is found among the top 1 retrieved documents. 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This metric measures the ability to reason about experimental videos in areas like immunology, drug delivery, and oncology.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%) - Medicine", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "SciVideoBench: Accuracy on Medicine Videos" + }, + "metric_id": "scivideobench_accuracy_on_medicine_videos", + "metric_name": "SciVideoBench: Accuracy on Medicine Videos", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 31.8 + }, + "evaluation_result_id": "SciVideoBench/GPT-4o/1771591481.616601#scivideobench#scivideobench_accuracy_on_medicine_videos" + }, + { + "evaluation_name": "SciVideoBench", + "source_data": { + "dataset_name": "SciVideoBench", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/groundmore/scivideobench" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Model performance on the subset of SciVideoBench questions related to the discipline of Physics. This metric measures the ability to reason about experimental videos in areas like acoustofluidics, condensed matter physics, and materials science.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%) - Physics", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "SciVideoBench: Accuracy on Physics Videos" + }, + "metric_id": "scivideobench_accuracy_on_physics_videos", + "metric_name": "SciVideoBench: Accuracy on Physics Videos", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 24.4 + }, + "evaluation_result_id": "SciVideoBench/GPT-4o/1771591481.616601#scivideobench#scivideobench_accuracy_on_physics_videos" + }, + { + "evaluation_name": "SciVideoBench", + "source_data": { + "dataset_name": "SciVideoBench", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/groundmore/scivideobench" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Model performance on the subset of SciVideoBench questions related to the discipline of Biology. 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This serves as a baseline to compare against the more robust MMMU-Pro benchmark and shows the performance drop when shortcuts are mitigated.", + "additional_details": { + "alphaxiv_y_axis": "MMMU(Val) Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MMMU Benchmark (Validation Set)" + }, + "metric_id": "mmmu_benchmark_validation_set", + "metric_name": "MMMU Benchmark (Validation Set)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 76 + }, + "evaluation_result_id": "MMMU-Pro/Skywork-R1V3-38B/1771591481.616601#mmmu_pro#mmmu_benchmark_validation_set" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/1a/fd/1afdca9c-93e1-469a-928a-faf908c7282a.json b/flat/objects/1a/fd/1afdca9c-93e1-469a-928a-faf908c7282a.json new file mode 100644 index 0000000000000000000000000000000000000000..f0802e2c8d72a964bb50f65b782d62881699cbc7 --- /dev/null +++ b/flat/objects/1a/fd/1afdca9c-93e1-469a-928a-faf908c7282a.json @@ -0,0 +1,358 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "RAG-QA Arena/LLAMA-3-8B/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": "University of California, Santa Barbara", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "LLAMA-3-8B", + "name": "LLAMA-3-8B", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The percentage of times a model's generated answer was preferred over the human-annotated LFRQA ground-truth answer. The evaluation was conducted on the full LFRQA test set, using the top 5 retrieved passages as context. Evaluation was performed by an LLM-based evaluator (GPT-4-0125-PREVIEW). Higher is better.", + "additional_details": { + "alphaxiv_y_axis": "Overall Win Rate (%)", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "Overall Win Rate (%) on LFRQA Test Set (Top 5 Passages)" + }, + "metric_id": "overall_win_rate_on_lfrqa_test_set_top_5_passages", + "metric_name": "Overall Win Rate (%) on LFRQA Test Set (Top 5 Passages)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 20.4 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#overall_win_rate_on_lfrqa_test_set_top_5_passages" + }, + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The percentage of queries for which the model responded with 'I couldn’t find an answer.' 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Lower is better.", + "additional_details": { + "alphaxiv_y_axis": "Overall No Answer Ratio (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Overall No Answer Ratio on LFRQA Test Set" + }, + "metric_id": "overall_no_answer_ratio_on_lfrqa_test_set", + "metric_name": "Overall No Answer Ratio on LFRQA Test Set", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 12.6 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#overall_no_answer_ratio_on_lfrqa_test_set" + }, + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The percentage of times a model's generated answer was preferred over or considered a tie with the human-annotated LFRQA ground-truth answer. The evaluation was conducted on the full LFRQA test set using the top 5 retrieved passages. Evaluation was performed by an LLM-based evaluator (GPT-4-0125-PREVIEW). Higher is better.", + "additional_details": { + "alphaxiv_y_axis": "Overall Win+Tie Rate (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Overall Win+Tie Rate (%) on LFRQA Test Set (Top 5 Passages)" + }, + "metric_id": "overall_win_tie_rate_on_lfrqa_test_set_top_5_passages", + "metric_name": "Overall Win+Tie Rate (%) on LFRQA Test Set (Top 5 Passages)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 23.5 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#overall_win_tie_rate_on_lfrqa_test_set_top_5_passages" + }, + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The percentage of times a model's generated answer was preferred over the LFRQA ground-truth answer for queries in the Biomedical domain. 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This measures domain-specific performance.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (%) - Finance", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Win Rate (%) on LFRQA Test Set - Finance Domain" + }, + "metric_id": "win_rate_on_lfrqa_test_set_finance_domain", + "metric_name": "Win Rate (%) on LFRQA Test Set - Finance Domain", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 24 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#win_rate_on_lfrqa_test_set_finance_domain" + }, + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The percentage of times a model's generated answer was preferred over the LFRQA ground-truth answer for queries in the Lifestyle domain. 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This measures domain-specific performance.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (%) - Recreation", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Win Rate (%) on LFRQA Test Set - Recreation Domain" + }, + "metric_id": "win_rate_on_lfrqa_test_set_recreation_domain", + "metric_name": "Win Rate (%) on LFRQA Test Set - Recreation Domain", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 19.4 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#win_rate_on_lfrqa_test_set_recreation_domain" + }, + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The percentage of times a model's generated answer was preferred over the LFRQA ground-truth answer for queries in the Science domain. This measures domain-specific performance.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (%) - Science", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Win Rate (%) on LFRQA Test Set - Science Domain" + }, + "metric_id": "win_rate_on_lfrqa_test_set_science_domain", + "metric_name": "Win Rate (%) on LFRQA Test Set - Science Domain", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 22.3 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#win_rate_on_lfrqa_test_set_science_domain" + }, + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The percentage of times a model's generated answer was preferred over the LFRQA ground-truth answer for queries in the Technology domain. This measures domain-specific performance.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (%) - Technology", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Win Rate (%) on LFRQA Test Set - Technology Domain" + }, + "metric_id": "win_rate_on_lfrqa_test_set_technology_domain", + "metric_name": "Win Rate (%) on LFRQA Test Set - Technology Domain", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 20.5 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#win_rate_on_lfrqa_test_set_technology_domain" + }, + { + "evaluation_name": "RAG-QA Arena", + "source_data": { + "dataset_name": "RAG-QA Arena", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.13998" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Elo rating calculated from pairwise comparisons against the LFRQA ground-truth answers. This system provides a relative skill level for each model's long-form answer generation capability. 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This measures domain-specific performance.", + "additional_details": { + "alphaxiv_y_axis": "Win Rate (%) - Writing", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Win Rate (%) on LFRQA Test Set - Writing Domain" + }, + "metric_id": "win_rate_on_lfrqa_test_set_writing_domain", + "metric_name": "Win Rate (%) on LFRQA Test Set - Writing Domain", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 12.5 + }, + "evaluation_result_id": "RAG-QA Arena/LLAMA-3-8B/1771591481.616601#rag_qa_arena#win_rate_on_lfrqa_test_set_writing_domain" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/1a/fe/1afe08b6-3ab2-4f2f-b574-a4b1282138ce.json b/flat/objects/1a/fe/1afe08b6-3ab2-4f2f-b574-a4b1282138ce.json new file mode 100644 index 0000000000000000000000000000000000000000..4f225e4df06544b7fa29978fbd0c91f6287fdc79 --- /dev/null +++ b/flat/objects/1a/fe/1afe08b6-3ab2-4f2f-b574-a4b1282138ce.json @@ -0,0 +1,148 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "QuestBench/o1-preview/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": "Google DeepMind", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "o1-preview", + "name": "o1-preview", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "QuestBench", + "source_data": { + "dataset_name": "QuestBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.22674" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of LLMs in selecting the correct clarifying question for underspecified Blocks World planning problems. This domain is highlighted as particularly challenging for modern models. Results are from a zero-shot (ZS) setting. (From Table 2)", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "QuestBench: Question-Asking Accuracy on Planning-Q (Zero-Shot)" + }, + "metric_id": "questbench_question_asking_accuracy_on_planning_q_zero_shot", + "metric_name": "QuestBench: Question-Asking Accuracy on Planning-Q (Zero-Shot)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 42.31 + }, + "evaluation_result_id": "QuestBench/o1-preview/1771591481.616601#questbench#questbench_question_asking_accuracy_on_planning_q_zero_shot" + }, + { + "evaluation_name": "QuestBench", + "source_data": { + "dataset_name": "QuestBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.22674" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of LLMs in selecting the correct clarifying question for underspecified equation-based grade-school math problems. Models perform very well on this task. Results are from a zero-shot (ZS) setting. (From Table 2)", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%) - GSME-Q (ZS)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "QuestBench: Question-Asking Accuracy on GSME-Q (Zero-Shot)" + }, + "metric_id": "questbench_question_asking_accuracy_on_gsme_q_zero_shot", + "metric_name": "QuestBench: Question-Asking Accuracy on GSME-Q (Zero-Shot)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 98.01 + }, + "evaluation_result_id": "QuestBench/o1-preview/1771591481.616601#questbench#questbench_question_asking_accuracy_on_gsme_q_zero_shot" + }, + { + "evaluation_name": "QuestBench", + "source_data": { + "dataset_name": "QuestBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.22674" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of LLMs in selecting the correct clarifying question for underspecified verbalized grade-school math word problems. Models perform well on this task, but slightly worse than on the equation-based version (GSME-Q). Results are from a zero-shot (ZS) setting. (From Table 2)", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%) - GSM-Q (ZS)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "QuestBench: Question-Asking Accuracy on GSM-Q (Zero-Shot)" + }, + "metric_id": "questbench_question_asking_accuracy_on_gsm_q_zero_shot", + "metric_name": "QuestBench: Question-Asking Accuracy on GSM-Q (Zero-Shot)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 91.32 + }, + "evaluation_result_id": "QuestBench/o1-preview/1771591481.616601#questbench#questbench_question_asking_accuracy_on_gsm_q_zero_shot" + }, + { + "evaluation_name": "QuestBench", + "source_data": { + "dataset_name": "QuestBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.22674" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of LLMs in selecting the correct clarifying question for underspecified propositional logic problems. 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This evaluation uses the 'Descriptive' instruction set from the CANITEDIT benchmark, which provides comprehensive, detailed specifications for the required code modification. 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This metric highlights the model's potential performance when multiple generations are possible.", + "additional_details": { + "alphaxiv_y_axis": "pass@100 (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Code Editing Accuracy on CANITEDIT (Descriptive, 100 Samples)" + }, + "metric_id": "code_editing_accuracy_on_canitedit_descriptive_100_samples", + "metric_name": "Code Editing Accuracy on CANITEDIT (Descriptive, 100 Samples)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 70.48 + }, + "evaluation_result_id": "CanItEdit/StarCoder/1771591481.616601#canitedit#code_editing_accuracy_on_canitedit_descriptive_100_samples" + }, + { + "evaluation_name": "CanItEdit", + "source_data": { + "dataset_name": "CanItEdit", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2312.12450" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures `pass@1` correctness on the 'Lazy' instruction set, which provides minimal direction and requires the model to infer more of the required actions. 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This is a notoriously difficult task for many T2I models.", + "additional_details": { + "alphaxiv_y_axis": "Text Score (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "UniGenBench Performance: Text Rendering" + }, + "metric_id": "unigenbench_performance_text_rendering", + "metric_name": "UniGenBench Performance: Text Rendering", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 13.83 + }, + "evaluation_result_id": "UniGenBench/wan2.2-t2i-plus/1771591481.616601#unigenbench#unigenbench_performance_text_rendering" + }, + { + "evaluation_name": "UniGenBench", + "source_data": { + "dataset_name": "UniGenBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2508.20751" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates if a model can accurately depict subjects performing the specified actions, including hand gestures, full-body poses, and interactions between objects (contact and non-contact).", + "additional_details": { + "alphaxiv_y_axis": "Action Score (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "UniGenBench Performance: Action" + }, + "metric_id": "unigenbench_performance_action", + "metric_name": "UniGenBench Performance: Action", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 68 + }, + "evaluation_result_id": "UniGenBench/wan2.2-t2i-plus/1771591481.616601#unigenbench#unigenbench_performance_action" + }, + { + "evaluation_name": "UniGenBench", + "source_data": { + "dataset_name": "UniGenBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2508.20751" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates a model's ability to generate images that correctly reflect factual knowledge about the world, such as historical events, scientific concepts, and cultural references.", + "additional_details": { + "alphaxiv_y_axis": "World Knowledge Score (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "UniGenBench Performance: World Knowledge" + }, + "metric_id": "unigenbench_performance_world_knowledge", + "metric_name": "UniGenBench Performance: World Knowledge", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 87.34 + }, + "evaluation_result_id": "UniGenBench/wan2.2-t2i-plus/1771591481.616601#unigenbench#unigenbench_performance_world_knowledge" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/28/7b/287bc499-042b-485c-9097-fe91124f315d.json b/flat/objects/28/7b/287bc499-042b-485c-9097-fe91124f315d.json new file mode 100644 index 0000000000000000000000000000000000000000..8cfd7874ea09dc32f17ec3368910517f8439f8a3 --- /dev/null +++ b/flat/objects/28/7b/287bc499-042b-485c-9097-fe91124f315d.json @@ -0,0 +1,383 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "helm_lite/mistralai_mistral-7b-instruct-v0.3/1777589798.2391284", + "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 Instruct v0.3 7B", + "id": "mistralai/mistral-7b-instruct-v0.3", + "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": 0.196, + "details": { + "description": "", + "tab": "Accuracy", + "Mean win rate - 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Observed inference time (s)": "{\"description\": \"min=0.221, mean=0.372, max=0.487, sum=1.862 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.37230395750413864\"}", + "MMLU - # eval": "{\"description\": \"min=100, mean=102.8, max=114, sum=514 (5)\", \"tab\": \"General information\", \"score\": \"102.8\"}", + "MMLU - # train": "{\"description\": \"min=5, mean=5, max=5, sum=25 (5)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "MMLU - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MMLU - # prompt tokens": "{\"description\": \"min=411.44, mean=532.091, max=696.175, sum=2660.455 (5)\", \"tab\": \"General information\", \"score\": \"532.0910877192983\"}", + "MMLU - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "[\"abstract_algebra\", \"college_chemistry\", \"computer_security\", \"econometrics\", \"us_foreign_policy\"]", + "method": "\"multiple_choice_joint\"" + } + } + }, + { + "evaluation_name": "MATH", + "source_data": { + "dataset_name": "MATH", + "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": "Equivalent (CoT) on MATH", + "metric_name": "Equivalent (CoT)", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.289, + "details": { + "description": "min=0.115, mean=0.289, max=0.477, sum=2.02 (7)", + "tab": "Accuracy", + "MATH - Observed inference time (s)": "{\"description\": \"min=2.027, mean=2.656, max=3.039, sum=18.593 (7)\", \"tab\": \"Efficiency\", \"score\": \"2.656151831465352\"}", + "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=123.616, mean=149.99, max=172.789, sum=1049.933 (7)\", \"tab\": \"General information\", \"score\": \"149.99043902740354\"}" + } + }, + "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": 0.538, + "details": { + "description": "min=0.538, mean=0.538, max=0.538, sum=0.538 (1)", + "tab": "Accuracy", + "GSM8K - 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Observed inference time (s)": "{\"description\": \"min=0.316, mean=0.489, max=0.855, sum=2.444 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.4887186054518059\"}", + "LegalBench - # eval": "{\"description\": \"min=95, mean=409.4, max=1000, sum=2047 (5)\", \"tab\": \"General information\", \"score\": \"409.4\"}", + "LegalBench - # train": "{\"description\": \"min=4, mean=4.8, max=5, sum=24 (5)\", \"tab\": \"General information\", \"score\": \"4.8\"}", + "LegalBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "LegalBench - # prompt tokens": "{\"description\": \"min=236.453, mean=1750.748, max=7224.488, sum=8753.741 (5)\", \"tab\": \"General information\", \"score\": \"1750.7482458432962\"}", + "LegalBench - # output tokens": "{\"description\": \"min=2, mean=9.174, max=15.242, sum=45.871 (5)\", \"tab\": \"General information\", \"score\": \"9.17419274343898\"}" + } + }, + "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": 0.517, + "details": { + "description": "min=0.517, mean=0.517, max=0.517, sum=0.517 (1)", + "tab": "Accuracy", + "MedQA - 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This task evaluates a model's world knowledge in elementary-level science with one in-context example.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorOpenBookQA (Bokmål) 1-Shot Accuracy" + }, + "metric_id": "noropenbookqa_bokm_l_1_shot_accuracy", + "metric_name": "NorOpenBookQA (Bokmål) 1-Shot Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 43.32 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#noropenbookqa_bokm_l_1_shot_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "4-shot accuracy on the multiple-choice NorOpenBookQA (NOBQA) dataset in Norwegian Bokmål (NB). This task evaluates a model's world knowledge in elementary-level science with four in-context examples.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorOpenBookQA (Bokmål) 4-Shot Accuracy" + }, + "metric_id": "noropenbookqa_bokm_l_4_shot_accuracy", + "metric_name": "NorOpenBookQA (Bokmål) 4-Shot Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 43.05 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#noropenbookqa_bokm_l_4_shot_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Zero-shot accuracy on the multiple-choice NorOpenBookQA (NOBQA) dataset in Norwegian Nynorsk (NN). This task evaluates a model's world knowledge in elementary-level science.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorOpenBookQA (Nynorsk) 0-Shot Accuracy" + }, + "metric_id": "noropenbookqa_nynorsk_0_shot_accuracy", + "metric_name": "NorOpenBookQA (Nynorsk) 0-Shot Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 33.33 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#noropenbookqa_nynorsk_0_shot_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "16-shot accuracy on the multiple-choice NorOpenBookQA (NOBQA) dataset in Norwegian Nynorsk (NN). This task evaluates a model's world knowledge in elementary-level science with sixteen in-context examples.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorOpenBookQA (Nynorsk) 16-Shot Accuracy" + }, + "metric_id": "noropenbookqa_nynorsk_16_shot_accuracy", + "metric_name": "NorOpenBookQA (Nynorsk) 16-Shot Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 32.22 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#noropenbookqa_nynorsk_16_shot_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "1-shot accuracy on the multiple-choice NorOpenBookQA (NOBQA) dataset in Norwegian Nynorsk (NN). This task evaluates a model's world knowledge in elementary-level science with one in-context example.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorOpenBookQA (Nynorsk) 1-Shot Accuracy" + }, + "metric_id": "noropenbookqa_nynorsk_1_shot_accuracy", + "metric_name": "NorOpenBookQA (Nynorsk) 1-Shot Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 28.89 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#noropenbookqa_nynorsk_1_shot_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "4-shot accuracy on the multiple-choice NorOpenBookQA (NOBQA) dataset in Norwegian Nynorsk (NN). This task evaluates a model's world knowledge in elementary-level science with four in-context examples.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorOpenBookQA (Nynorsk) 4-Shot Accuracy" + }, + "metric_id": "noropenbookqa_nynorsk_4_shot_accuracy", + "metric_name": "NorOpenBookQA (Nynorsk) 4-Shot Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 31.11 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#noropenbookqa_nynorsk_4_shot_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Zero-shot ROUGE-L score on the generation version of the NorTruthfulQA (NTRQA) dataset in Norwegian Bokmål (NB). This task assesses whether a model generates truthful free-form answers, measured against correct reference answers.", + "additional_details": { + "alphaxiv_y_axis": "ROUGE-L", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorTruthfulQA Generation (Bokmål) ROUGE-L" + }, + "metric_id": "nortruthfulqa_generation_bokm_l_rouge_l", + "metric_name": "NorTruthfulQA Generation (Bokmål) ROUGE-L", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 28.66 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#nortruthfulqa_generation_bokm_l_rouge_l" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Zero-shot ROUGE-L score on the generation version of the NorTruthfulQA (NTRQA) dataset in Norwegian Nynorsk (NN). This task assesses whether a model generates truthful free-form answers, measured against correct reference answers.", + "additional_details": { + "alphaxiv_y_axis": "ROUGE-L", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorTruthfulQA Generation (Nynorsk) ROUGE-L" + }, + "metric_id": "nortruthfulqa_generation_nynorsk_rouge_l", + "metric_name": "NorTruthfulQA Generation (Nynorsk) ROUGE-L", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 28.66 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#nortruthfulqa_generation_nynorsk_rouge_l" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Zero-shot accuracy on the multiple-choice version of the NorTruthfulQA (NTRQA) dataset in Norwegian Bokmål (NB). This task assesses a model's ability to identify truthful statements and avoid common misconceptions.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorTruthfulQA Multiple-Choice (Bokmål) Accuracy" + }, + "metric_id": "nortruthfulqa_multiple_choice_bokm_l_accuracy", + "metric_name": "NorTruthfulQA Multiple-Choice (Bokmål) Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 62.91 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#nortruthfulqa_multiple_choice_bokm_l_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Zero-shot accuracy on the multiple-choice version of the NorTruthfulQA (NTRQA) dataset in Norwegian Nynorsk (NN). This task assesses a model's ability to identify truthful statements and avoid common misconceptions.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorTruthfulQA Multiple-Choice (Nynorsk) Accuracy" + }, + "metric_id": "nortruthfulqa_multiple_choice_nynorsk_accuracy", + "metric_name": "NorTruthfulQA Multiple-Choice (Nynorsk) Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 61.4 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#nortruthfulqa_multiple_choice_nynorsk_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Zero-shot accuracy on the multiple-choice NorCommonSenseQA (NCSQA) dataset in Norwegian Bokmål (NB). This task assesses a model's commonsense reasoning abilities.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NorCommonSenseQA (Norwegian Bokmål) Accuracy" + }, + "metric_id": "norcommonsenseqa_norwegian_bokm_l_accuracy", + "metric_name": "NorCommonSenseQA (Norwegian Bokmål) Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 43.89 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#norcommonsenseqa_norwegian_bokm_l_accuracy" + }, + { + "evaluation_name": "NorQA", + "source_data": { + "dataset_name": "NorQA", + "source_type": "url", + "url": [ + "https://huggingface.co/datasets/apple/mkqa" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Zero-shot accuracy on the multiple-choice NRK-Quiz-QA dataset in Norwegian Nynorsk (NN). This task evaluates a model's Norwegian-specific and world knowledge using questions from Norway's national public broadcaster.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "NRK-Quiz-QA (Norwegian Nynorsk) Accuracy" + }, + "metric_id": "nrk_quiz_qa_norwegian_nynorsk_accuracy", + "metric_name": "NRK-Quiz-QA (Norwegian Nynorsk) Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 53.53 + }, + "evaluation_result_id": "NorQA/NorBLOOM-7B-scratch/1771591481.616601#norqa#nrk_quiz_qa_norwegian_nynorsk_accuracy" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/74/7a/747a2168-af20-4f8a-a003-fa5d3d24a917.json b/flat/objects/74/7a/747a2168-af20-4f8a-a003-fa5d3d24a917.json new file mode 100644 index 0000000000000000000000000000000000000000..d534309de71dea5d64682a34792b9f3d38f022cd --- /dev/null +++ b/flat/objects/74/7a/747a2168-af20-4f8a-a003-fa5d3d24a917.json @@ -0,0 +1,58 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "FineGRAIN/Pixtral-124B/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": "Sony AI", + "alphaxiv_dataset_type": "image", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Pixtral-124B", + "name": "Pixtral-124B", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "FineGRAIN", + "source_data": { + "dataset_name": "FineGRAIN", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2512.02161" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of Vision-Language Models (VLMs) in identifying failures in images generated by Text-to-Image models. 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Part of the CharXiv benchmark's validation set.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%) (Compositionality)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CharXiv: Descriptive - Compositionality Accuracy" + }, + "metric_id": "charxiv_descriptive_compositionality_accuracy", + "metric_name": "CharXiv: Descriptive - Compositionality Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 6.7 + }, + "evaluation_result_id": "CharXiv/CogAgent/1771591481.616601#charxiv#charxiv_descriptive_compositionality_accuracy" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/74/93/74939aeb-a2c9-4c46-af9e-ca7bbfed07ee.json b/flat/objects/74/93/74939aeb-a2c9-4c46-af9e-ca7bbfed07ee.json new file mode 100644 index 0000000000000000000000000000000000000000..0736fe595333f034587985aef410faa8aecd6a1c --- /dev/null +++ b/flat/objects/74/93/74939aeb-a2c9-4c46-af9e-ca7bbfed07ee.json @@ -0,0 +1,58 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "MCLM/Qwen2.5-Math-1.5B-OREO/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": "Yonsei University", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Qwen2.5-Math-1.5B-OREO", + "name": "Qwen2.5-Math-1.5B-OREO", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "MCLM", + "source_data": { + "dataset_name": "MCLM", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17407" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the MATH-100 subset, used to validate that this 100-question sample is a reliable proxy for the full 500-question MATH-500 dataset. This comparison (from Table 6) confirms the representativeness of the MT-MATH100 subset used in the main MCLM benchmark.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%) on MATH-100", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MATH-100 Subset Performance (Validation)" + }, + "metric_id": "math_100_subset_performance_validation", + "metric_name": "MATH-100 Subset Performance (Validation)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 38.45 + }, + "evaluation_result_id": "MCLM/Qwen2.5-Math-1.5B-OREO/1771591481.616601#mclm#math_100_subset_performance_validation" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/74/99/7499474a-bd8d-4f05-b1fa-9c444bc3d1fd.json b/flat/objects/74/99/7499474a-bd8d-4f05-b1fa-9c444bc3d1fd.json new file mode 100644 index 0000000000000000000000000000000000000000..af73de5aa986ca5dacdc19ebe4f46bbb221ecf6e --- /dev/null +++ b/flat/objects/74/99/7499474a-bd8d-4f05-b1fa-9c444bc3d1fd.json @@ -0,0 +1,328 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "VulDetectBench/CodeLlama (13B)/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": "Fudan University", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "CodeLlama (13B)", + "name": "CodeLlama (13B)", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the F1 score of models in binary classification of whether a given C/C++ code snippet contains a vulnerability, providing a balance between precision and recall. This is a core metric for the fundamental Task 1 of VulDetectBench. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "F1 Score", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "Vulnerability Existence Detection (Task 1, F1 Score)" + }, + "metric_id": "vulnerability_existence_detection_task_1_f1_score", + "metric_name": "Vulnerability Existence Detection (Task 1, F1 Score)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 58.81 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#vulnerability_existence_detection_task_1_f1_score" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the Micro Recall (MIR) for identifying specific data objects and function calls associated with a vulnerability. 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Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Micro Recall (MIR)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Key Objects & Functions Identification (Task 3, Micro Recall)" + }, + "metric_id": "key_objects_functions_identification_task_3_micro_recall", + "metric_name": "Key Objects & Functions Identification (Task 3, Micro Recall)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 8.51 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#key_objects_functions_identification_task_3_micro_recall" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates models on identifying the specific Common Weakness Enumeration (CWE) type of a vulnerability from multiple choices. The Moderate Evaluation (ME) score awards 1 point for selecting either the optimal or suboptimal choice. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Moderate Evaluation (ME) Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Vulnerability CWE Type Inference (Task 2, Moderate Evaluation)" + }, + "metric_id": "vulnerability_cwe_type_inference_task_2_moderate_evaluation", + "metric_name": "Vulnerability CWE Type Inference (Task 2, Moderate Evaluation)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 44.6 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#vulnerability_cwe_type_inference_task_2_moderate_evaluation" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates models on identifying the specific Common Weakness Enumeration (CWE) type of a vulnerability from multiple choices. The Strict Evaluation (SE) score awards 1 point for the optimal (actual) CWE type and 0.5 points for a suboptimal (ancestor) choice. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Strict Evaluation (SE) Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Vulnerability CWE Type Inference (Task 2, Strict Evaluation)" + }, + "metric_id": "vulnerability_cwe_type_inference_task_2_strict_evaluation", + "metric_name": "Vulnerability CWE Type Inference (Task 2, Strict Evaluation)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 32.8 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#vulnerability_cwe_type_inference_task_2_strict_evaluation" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of models in binary classification of whether a given C/C++ code snippet contains a vulnerability. This is the first and most fundamental task in the VulDetectBench benchmark. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Vulnerability Existence Detection (Task 1, Accuracy)" + }, + "metric_id": "vulnerability_existence_detection_task_1_accuracy", + "metric_name": "Vulnerability Existence Detection (Task 1, Accuracy)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 47.9 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#vulnerability_existence_detection_task_1_accuracy" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the Output Recall Score (ORS) for precisely locating the vulnerability's root cause. 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This is a challenging localization task. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Union Recall Score (URS)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Vulnerability Root Cause Location (Task 4, URS)" + }, + "metric_id": "vulnerability_root_cause_location_task_4_urs", + "metric_name": "Vulnerability Root Cause Location (Task 4, URS)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 3.3 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#vulnerability_root_cause_location_task_4_urs" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the Output Recall Score (ORS) for precisely identifying the vulnerability's trigger point. ORS helps evaluate compliance with output instructions. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Output Recall Score (ORS) - Trigger Point", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Vulnerability Trigger Point Location (Task 5, ORS)" + }, + "metric_id": "vulnerability_trigger_point_location_task_5_ors", + "metric_name": "Vulnerability Trigger Point Location (Task 5, ORS)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 1.89 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#vulnerability_trigger_point_location_task_5_ors" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the Macro Recall (MAR) for identifying specific data objects and function calls that are critically associated with a vulnerability. This task tests a model's ability to localize key components. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Macro Recall (MAR)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Key Objects & Functions Identification (Task 3, Macro Recall)" + }, + "metric_id": "key_objects_functions_identification_task_3_macro_recall", + "metric_name": "Key Objects & Functions Identification (Task 3, Macro Recall)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 10.34 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#key_objects_functions_identification_task_3_macro_recall" + }, + { + "evaluation_name": "VulDetectBench", + "source_data": { + "dataset_name": "VulDetectBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.07595" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the Union Recall Score (URS) for precisely identifying the specific line(s) of code where the vulnerability is triggered. This is the most granular localization task in the benchmark. Results are from Table 3.", + "additional_details": { + "alphaxiv_y_axis": "Union Recall Score (URS) - Trigger Point", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Vulnerability Trigger Point Location (Task 5, URS)" + }, + "metric_id": "vulnerability_trigger_point_location_task_5_urs", + "metric_name": "Vulnerability Trigger Point Location (Task 5, URS)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 1.29 + }, + "evaluation_result_id": "VulDetectBench/CodeLlama (13B)/1771591481.616601#vuldetectbench#vulnerability_trigger_point_location_task_5_urs" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/74/9b/749b2b3d-cc40-4160-b61a-866f79e09736.json b/flat/objects/74/9b/749b2b3d-cc40-4160-b61a-866f79e09736.json new file mode 100644 index 0000000000000000000000000000000000000000..6bf97b87efeb50afeb5cde4e0a0d3abd05659416 --- /dev/null +++ b/flat/objects/74/9b/749b2b3d-cc40-4160-b61a-866f79e09736.json @@ -0,0 +1,208 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "MINTQA/Phi-3-mini/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 Manchester", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Phi-3-mini", + "name": "Phi-3-mini", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "MINTQA", + "source_data": { + "dataset_name": "MINTQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.17032" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates model accuracy on the MINTQA-TI dataset (new knowledge) using the 'Generate then Adaptively Retrieve' strategy. In this setting, models dynamically decide whether to retrieve external information for each sub-question they generate. This result uses the PromptRetrieval retriever. This task is the most comprehensive, combining decomposition, decision-making, and RAG on new knowledge.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "Adaptive Retrieval Accuracy on MINTQA-TI (New Knowledge) with PromptRetrieval" + }, + "metric_id": "adaptive_retrieval_accuracy_on_mintqa_ti_new_knowledge_with_promptretrieval", + "metric_name": "Adaptive Retrieval Accuracy on MINTQA-TI (New Knowledge) with PromptRetrieval", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 17.21 + }, + "evaluation_result_id": "MINTQA/Phi-3-mini/1771591481.616601#mintqa#adaptive_retrieval_accuracy_on_mintqa_ti_new_knowledge_with_promptretrieval" + }, + { + "evaluation_name": "MINTQA", + "source_data": { + "dataset_name": "MINTQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.17032" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates model accuracy on the MINTQA-POP dataset where models must first generate their own sub-questions to break down the main multi-hop question, and then answer them to arrive at a final solution. This tests the end-to-end decomposition and reasoning ability.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Decomposition Accuracy on MINTQA-POP (Self-Generated Sub-questions)" + }, + "metric_id": "decomposition_accuracy_on_mintqa_pop_self_generated_sub_questions", + "metric_name": "Decomposition Accuracy on MINTQA-POP (Self-Generated Sub-questions)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 21.41 + }, + "evaluation_result_id": "MINTQA/Phi-3-mini/1771591481.616601#mintqa#decomposition_accuracy_on_mintqa_pop_self_generated_sub_questions" + }, + { + "evaluation_name": "MINTQA", + "source_data": { + "dataset_name": "MINTQA", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2412.17032" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates model accuracy on the MINTQA-TI (new knowledge) dataset where models must first generate their own sub-questions to break down the main multi-hop question, and then answer them. 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Observed inference time (s)": "{\"description\": \"min=1.329, mean=1.329, max=1.329, sum=1.329 (1)\", \"tab\": \"Efficiency\", \"score\": \"1.328731627702713\"}", + "NaturalQuestions (closed-book) - Observed inference time (s)": "{\"description\": \"min=0.802, mean=0.802, max=0.802, sum=0.802 (1)\", \"tab\": \"Efficiency\", \"score\": \"0.8020290625095368\"}", + "NaturalQuestions (open-book) - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "NaturalQuestions (open-book) - # train": "{\"description\": \"min=4.717, mean=4.717, max=4.717, sum=4.717 (1)\", \"tab\": \"General information\", \"score\": \"4.717\"}", + "NaturalQuestions (open-book) - truncated": "{\"description\": \"min=0.038, mean=0.038, max=0.038, sum=0.038 (1)\", \"tab\": \"General information\", \"score\": \"0.038\"}", + "NaturalQuestions (open-book) - # prompt tokens": "{\"description\": \"min=1488.14, mean=1488.14, max=1488.14, sum=1488.14 (1)\", \"tab\": \"General information\", \"score\": \"1488.14\"}", + "NaturalQuestions (open-book) - # output tokens": "{\"description\": \"min=10.866, mean=10.866, max=10.866, sum=10.866 (1)\", \"tab\": \"General information\", \"score\": \"10.866\"}", + "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=116.087, mean=116.087, max=116.087, sum=116.087 (1)\", \"tab\": \"General information\", \"score\": \"116.087\"}", + "NaturalQuestions (closed-book) - # output tokens": "{\"description\": \"min=5.908, mean=5.908, max=5.908, sum=5.908 (1)\", \"tab\": \"General information\", \"score\": \"5.908\"}" + } + }, + "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": 0.286, + "details": { + "description": "min=0.286, mean=0.286, max=0.286, sum=0.286 (1)", + "tab": "Accuracy", + "OpenbookQA - Observed inference time (s)": "{\"description\": \"min=0.667, mean=0.667, max=0.667, sum=0.667 (1)\", \"tab\": \"Efficiency\", \"score\": \"0.6669360423088073\"}", + "OpenbookQA - # eval": "{\"description\": \"min=500, mean=500, max=500, sum=500 (1)\", \"tab\": \"General information\", \"score\": \"500.0\"}", + "OpenbookQA - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "OpenbookQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "OpenbookQA - # prompt tokens": "{\"description\": \"min=254.652, mean=254.652, max=254.652, sum=254.652 (1)\", \"tab\": \"General information\", \"score\": \"254.652\"}", + "OpenbookQA - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "dataset": "\"openbookqa\"", + "method": "\"multiple_choice_joint\"" + } + } + }, + { + "evaluation_name": "MMLU", + "source_data": { + "dataset_name": "MMLU", + "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 MMLU", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.243, + "details": { + "description": "min=0.22, mean=0.243, max=0.29, sum=1.217 (5)", + "tab": "Accuracy", + "MMLU - Observed inference time (s)": "{\"description\": \"min=0.619, mean=0.632, max=0.648, sum=3.162 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.6324507230122884\"}", + "MMLU - # eval": "{\"description\": \"min=100, mean=102.8, max=114, sum=514 (5)\", \"tab\": \"General information\", \"score\": \"102.8\"}", + "MMLU - # train": "{\"description\": \"min=5, mean=5, max=5, sum=25 (5)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "MMLU - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MMLU - # prompt tokens": "{\"description\": \"min=360.75, mean=471.075, max=618.447, sum=2355.377 (5)\", \"tab\": \"General information\", \"score\": \"471.0754736842106\"}", + "MMLU - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "[\"abstract_algebra\", \"college_chemistry\", \"computer_security\", \"econometrics\", \"us_foreign_policy\"]", + "method": "\"multiple_choice_joint\"" + } + } + }, + { + "evaluation_name": "MATH", + "source_data": { + "dataset_name": "MATH", + "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": "Equivalent (CoT) on MATH", + "metric_name": "Equivalent (CoT)", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.026, + "details": { + "description": "min=0, mean=0.026, max=0.067, sum=0.184 (7)", + "tab": "Accuracy", + "MATH - Observed inference time (s)": "{\"description\": \"min=5.282, mean=9.204, max=20.088, sum=64.425 (7)\", \"tab\": \"Efficiency\", \"score\": \"9.203530075671766\"}", + "MATH - # eval": "{\"description\": \"min=30, mean=62.429, max=135, sum=437 (7)\", \"tab\": \"General information\", \"score\": \"62.42857142857143\"}", + "MATH - # train": "{\"description\": \"min=2.962, mean=6.916, max=8, sum=48.409 (7)\", \"tab\": \"General information\", \"score\": \"6.915558126084441\"}", + "MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (7)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MATH - # prompt tokens": "{\"description\": \"min=928.719, mean=1184.139, max=1546.442, sum=8288.975 (7)\", \"tab\": \"General information\", \"score\": \"1184.139339428874\"}", + "MATH - # output tokens": "{\"description\": \"min=114.077, mean=139.637, max=180.663, sum=977.456 (7)\", \"tab\": \"General information\", \"score\": \"139.6365272403828\"}" + } + }, + "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": 0.028, + "details": { + "description": "min=0.028, mean=0.028, max=0.028, sum=0.028 (1)", + "tab": "Accuracy", + "GSM8K - Observed inference time (s)": "{\"description\": \"min=16.427, mean=16.427, max=16.427, sum=16.427 (1)\", \"tab\": \"Efficiency\", \"score\": \"16.42652773284912\"}", + "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=943.121, mean=943.121, max=943.121, sum=943.121 (1)\", \"tab\": \"General information\", \"score\": \"943.121\"}", + "GSM8K - # output tokens": "{\"description\": \"min=400, mean=400, max=400, sum=400 (1)\", \"tab\": \"General information\", \"score\": \"400.0\"}" + } + }, + "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": 0.332, + "details": { + "description": "min=0.165, mean=0.332, max=0.601, sum=1.659 (5)", + "tab": "Accuracy", + "LegalBench - Observed inference time (s)": "{\"description\": \"min=0.636, mean=0.753, max=1.073, sum=3.767 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.7533007583490331\"}", + "LegalBench - # eval": "{\"description\": \"min=95, mean=409.4, max=1000, sum=2047 (5)\", \"tab\": \"General information\", \"score\": \"409.4\"}", + "LegalBench - # train": "{\"description\": \"min=0.335, mean=3.867, max=5, sum=19.335 (5)\", \"tab\": \"General information\", \"score\": \"3.866938775510204\"}", + "LegalBench - truncated": "{\"description\": \"min=0, mean=0.133, max=0.665, sum=0.665 (5)\", \"tab\": \"General information\", \"score\": \"0.1330612244897959\"}", + "LegalBench - # prompt tokens": "{\"description\": \"min=205.726, mean=566.59, max=1514.545, sum=2832.948 (5)\", \"tab\": \"General information\", \"score\": \"566.5895794484264\"}", + "LegalBench - # output tokens": "{\"description\": \"min=1, mean=1.639, max=4.027, sum=8.196 (5)\", \"tab\": \"General information\", \"score\": \"1.6391061224489796\"}" + } + }, + "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": 0.26, + "details": { + "description": "min=0.26, mean=0.26, max=0.26, sum=0.26 (1)", + "tab": "Accuracy", + "MedQA - Observed inference time (s)": "{\"description\": \"min=0.726, mean=0.726, max=0.726, sum=0.726 (1)\", \"tab\": \"Efficiency\", \"score\": \"0.7258754989972882\"}", + "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=1005.229, mean=1005.229, max=1005.229, sum=1005.229 (1)\", \"tab\": \"General information\", \"score\": \"1005.2286282306163\"}", + "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": 0.066, + "details": { + "description": "min=0.0, mean=0.066, max=0.171, sum=0.331 (5)", + "tab": "Accuracy", + "WMT 2014 - Observed inference time (s)": "{\"description\": \"min=4.671, mean=4.693, max=4.731, sum=23.465 (5)\", \"tab\": \"Efficiency\", \"score\": \"4.692985351748752\"}", + "WMT 2014 - # eval": "{\"description\": \"min=503, mean=568.8, max=832, sum=2844 (5)\", \"tab\": \"General information\", \"score\": \"568.8\"}", + "WMT 2014 - # train": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "WMT 2014 - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "WMT 2014 - # prompt tokens": "{\"description\": \"min=99.111, mean=157.232, max=255.504, sum=786.158 (5)\", \"tab\": \"General information\", \"score\": \"157.2315362631901\"}", + "WMT 2014 - # output tokens": "{\"description\": \"min=99.869, mean=99.974, max=100, sum=499.869 (5)\", \"tab\": \"General information\", \"score\": \"99.97375745526838\"}" + } + }, + "generation_config": { + "additional_details": { + "language_pair": "[\"cs-en\", \"de-en\", \"fr-en\", \"hi-en\", \"ru-en\"]" + } + } + } + ] +} \ No newline at end of file diff --git a/flat/objects/80/a8/80a8ab8b-fbf5-4d6a-95a3-ba06c7838091.json b/flat/objects/80/a8/80a8ab8b-fbf5-4d6a-95a3-ba06c7838091.json new file mode 100644 index 0000000000000000000000000000000000000000..836debb1a1cf692cb16e33cce3e7703e2fb042e3 --- /dev/null +++ b/flat/objects/80/a8/80a8ab8b-fbf5-4d6a-95a3-ba06c7838091.json @@ -0,0 +1,358 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "MultiChartQA/LLaVA-V1.6-7B/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": "University of Notre Dame", + "alphaxiv_dataset_type": "image", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "LLaVA-V1.6-7B", + "name": "LLaVA-V1.6-7B", + "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": 26.35 + }, + "evaluation_result_id": "MultiChartQA/LLaVA-V1.6-7B/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": 17.79 + }, + "evaluation_result_id": "MultiChartQA/LLaVA-V1.6-7B/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. 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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": 11.42 + }, + "evaluation_result_id": "MultiChartQA/Idefics3-8B/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. 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# output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "College Mathematics - # eval": "{\"description\": \"min=100, mean=100, max=100, sum=200 (2)\", \"tab\": \"General information\", \"score\": \"100.0\"}", + "College Mathematics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "College Mathematics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "College Mathematics - # prompt tokens": "{\"description\": \"min=607.7, mean=607.7, max=607.7, sum=1215.4 (2)\", \"tab\": \"General information\", \"score\": \"607.7\"}", + "College Mathematics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "College Medicine - # eval": "{\"description\": \"min=173, mean=173, max=173, sum=346 (2)\", \"tab\": \"General information\", \"score\": \"173.0\"}", + "College Medicine - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "College Medicine - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "College Medicine - # prompt tokens": "{\"description\": \"min=506.098, mean=506.098, max=506.098, sum=1012.197 (2)\", \"tab\": \"General information\", \"score\": \"506.0982658959538\"}", + "College Medicine - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "College Physics - # eval": "{\"description\": \"min=102, mean=102, max=102, sum=204 (2)\", \"tab\": \"General information\", \"score\": \"102.0\"}", + "College Physics - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "College Physics - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "College Physics - # prompt tokens": "{\"description\": \"min=516.265, mean=516.265, max=516.265, sum=1032.529 (2)\", \"tab\": \"General information\", \"score\": \"516.2647058823529\"}", + "College Physics - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"college_physics\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_college_physics\"" + } + } + }, + { + "evaluation_name": "Computer Security", + "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 Computer Security", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.79, + "details": { + "description": "min=0.79, mean=0.79, max=0.79, sum=1.58 (2)", + "tab": "Accuracy", + "Computer Security - Observed inference time (s)": "{\"description\": \"min=0.352, mean=0.352, max=0.352, sum=0.705 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3522661328315735\"}", + "Computer Security - # eval": "{\"description\": \"min=100, mean=100, max=100, sum=200 (2)\", \"tab\": \"General information\", \"score\": \"100.0\"}", + "Computer Security - # train": "{\"description\": \"min=5, mean=5, max=5, sum=10 (2)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "Computer Security - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (2)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "Computer Security - # prompt tokens": "{\"description\": \"min=386.64, mean=386.64, max=386.64, sum=773.28 (2)\", \"tab\": \"General information\", \"score\": \"386.64\"}", + "Computer Security - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=2 (2)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "\"computer_security\"", + "method": "\"multiple_choice_joint\"", + "eval_split": "\"test\"", + "groups": "\"mmlu_computer_security\"" + } + } + }, + { + "evaluation_name": "Econometrics", + "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 Econometrics", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.64, + "details": { + "description": "min=0.64, mean=0.64, max=0.64, sum=1.281 (2)", + "tab": "Accuracy", + "Econometrics - Observed inference time (s)": "{\"description\": \"min=0.346, mean=0.346, max=0.346, sum=0.691 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.34558368356604324\"}", + "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=627.939, mean=627.939, max=627.939, sum=1255.877 (2)\", \"tab\": \"General information\", \"score\": \"627.938596491228\"}", + "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": 0.42, + "details": { + "description": "min=0.42, mean=0.42, max=0.42, sum=0.84 (2)", + "tab": "Accuracy", + "Global Facts - Observed inference time (s)": "{\"description\": \"min=0.315, mean=0.315, max=0.315, sum=0.63 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.314766480922699\"}", + "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=429.06, mean=429.06, max=429.06, sum=858.12 (2)\", \"tab\": \"General information\", \"score\": \"429.06\"}", + "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": 0.796, + "details": { + "description": "min=0.796, mean=0.796, max=0.796, sum=1.593 (2)", + "tab": "Accuracy", + "Jurisprudence - Observed inference time (s)": "{\"description\": \"min=0.321, mean=0.321, max=0.321, sum=0.642 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.32116924391852486\"}", + "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=394.713, mean=394.713, max=394.713, sum=789.426 (2)\", \"tab\": \"General information\", \"score\": \"394.712962962963\"}", + "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": 0.746, + "details": { + "description": "min=0.746, mean=0.746, max=0.746, sum=1.492 (2)", + "tab": "Accuracy", + "Philosophy - Observed inference time (s)": "{\"description\": \"min=0.44, mean=0.44, max=0.44, sum=0.88 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.4401504610129108\"}", + "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=329.09, mean=329.09, max=329.09, sum=658.18 (2)\", \"tab\": \"General information\", \"score\": \"329.09003215434086\"}", + "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": 0.757, + "details": { + "description": "min=0.757, mean=0.757, max=0.757, sum=1.513 (2)", + "tab": "Accuracy", + "Professional Medicine - Observed inference time (s)": "{\"description\": \"min=0.394, mean=0.394, max=0.394, sum=0.788 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.393971232806935\"}", + "Professional Accounting - Observed inference time (s)": "{\"description\": \"min=0.185, mean=0.185, max=0.185, sum=0.371 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.18525678553479782\"}", + "Professional Law - Observed inference time (s)": "{\"description\": \"min=0.205, mean=0.205, max=0.205, sum=0.409 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.20459390463698485\"}", + "Professional Psychology - Observed inference time (s)": "{\"description\": \"min=0.166, mean=0.166, max=0.166, sum=0.332 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.16597708611706502\"}", + "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=1125.199, mean=1125.199, max=1125.199, sum=2250.397 (2)\", \"tab\": \"General information\", \"score\": \"1125.1985294117646\"}", + "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=739.34, mean=739.34, max=739.34, sum=1478.681 (2)\", \"tab\": \"General information\", \"score\": \"739.3404255319149\"}", + "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=1663.969, mean=1663.969, max=1663.969, sum=3327.939 (2)\", \"tab\": \"General information\", \"score\": \"1663.9693611473272\"}", + "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=581.417, mean=581.417, max=581.417, sum=1162.833 (2)\", \"tab\": \"General information\", \"score\": \"581.4166666666666\"}", + "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": 0.86, + "details": { + "description": "min=0.86, mean=0.86, max=0.86, sum=1.72 (2)", + "tab": "Accuracy", + "Us Foreign Policy - Observed inference time (s)": "{\"description\": \"min=0.33, mean=0.33, max=0.33, sum=0.66 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.33019849777221677\"}", + "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=428.16, mean=428.16, max=428.16, sum=856.32 (2)\", \"tab\": \"General information\", \"score\": \"428.16\"}", + "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": 0.836, + "details": { + "description": "min=0.836, mean=0.836, max=0.836, sum=1.671 (2)", + "tab": "Accuracy", + "Astronomy - Observed inference time (s)": "{\"description\": \"min=0.314, mean=0.314, max=0.314, sum=0.629 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3143457660549565\"}", + "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=589.849, mean=589.849, max=589.849, sum=1179.697 (2)\", \"tab\": \"General information\", \"score\": \"589.8486842105264\"}", + "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": 0.82, + "details": { + "description": "min=0.82, mean=0.82, max=0.82, sum=1.64 (2)", + "tab": "Accuracy", + "Business Ethics - Observed inference time (s)": "{\"description\": \"min=0.308, mean=0.308, max=0.308, sum=0.615 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3076848840713501\"}", + "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=569.87, mean=569.87, max=569.87, sum=1139.74 (2)\", \"tab\": \"General information\", \"score\": \"569.87\"}", + "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": 0.785, + "details": { + "description": "min=0.785, mean=0.785, max=0.785, sum=1.57 (2)", + "tab": "Accuracy", + "Clinical Knowledge - Observed inference time (s)": "{\"description\": \"min=0.335, mean=0.335, max=0.335, sum=0.67 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.33518469288664043\"}", + "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=400.623, mean=400.623, max=400.623, sum=801.245 (2)\", \"tab\": \"General information\", \"score\": \"400.62264150943395\"}", + "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": 0.736, + "details": { + "description": "min=0.736, mean=0.736, max=0.736, sum=1.472 (2)", + "tab": "Accuracy", + "Conceptual Physics - Observed inference time (s)": "{\"description\": \"min=0.253, mean=0.253, max=0.253, sum=0.506 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.2531234142628122\"}", + "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=305.494, mean=305.494, max=305.494, sum=610.987 (2)\", \"tab\": \"General information\", \"score\": \"305.4936170212766\"}", + "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": 0.717, + "details": { + "description": "min=0.717, mean=0.717, max=0.717, sum=1.434 (2)", + "tab": "Accuracy", + "Electrical Engineering - Observed inference time (s)": "{\"description\": \"min=0.198, mean=0.198, max=0.198, sum=0.396 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.19794883070320918\"}", + "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=463.8, mean=463.8, max=463.8, sum=927.6 (2)\", \"tab\": \"General information\", \"score\": \"463.8\"}", + "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": 0.643, + "details": { + "description": "min=0.643, mean=0.643, max=0.643, sum=1.286 (2)", + "tab": "Accuracy", + "Elementary Mathematics - Observed inference time (s)": "{\"description\": \"min=0.202, mean=0.202, max=0.202, sum=0.404 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.2021035529949047\"}", + "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=577.119, mean=577.119, max=577.119, sum=1154.238 (2)\", \"tab\": \"General information\", \"score\": \"577.1190476190476\"}", + "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": 0.587, + "details": { + "description": "min=0.587, mean=0.587, max=0.587, sum=1.175 (2)", + "tab": "Accuracy", + "Formal Logic - Observed inference time (s)": "{\"description\": \"min=0.197, mean=0.197, max=0.197, sum=0.393 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.196545644411965\"}", + "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=604.667, mean=604.667, max=604.667, sum=1209.333 (2)\", \"tab\": \"General information\", \"score\": \"604.6666666666666\"}", + "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": 0.878, + "details": { + "description": "min=0.878, mean=0.878, max=0.878, sum=1.755 (2)", + "tab": "Accuracy", + "High School Biology - Observed inference time (s)": "{\"description\": \"min=0.192, mean=0.192, max=0.192, sum=0.384 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.19177444058079873\"}", + "High School Chemistry - Observed inference time (s)": "{\"description\": \"min=0.236, mean=0.236, max=0.236, sum=0.472 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.23597407693346145\"}", + "High School Computer Science - Observed inference time (s)": "{\"description\": \"min=0.202, mean=0.202, max=0.202, sum=0.404 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.20180433988571167\"}", + "High School European History - Observed inference time (s)": "{\"description\": \"min=0.313, mean=0.313, max=0.313, sum=0.626 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.3130656791455818\"}", + "High School Geography - Observed inference time (s)": "{\"description\": \"min=0.215, mean=0.215, max=0.215, sum=0.43 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.21512896725625702\"}", + "High School Government And Politics - Observed inference time (s)": "{\"description\": \"min=0.192, mean=0.192, max=0.192, sum=0.384 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.19191643611137113\"}", + "High School Macroeconomics - Observed inference time (s)": "{\"description\": \"min=0.204, mean=0.204, max=0.204, sum=0.409 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.20429076965038592\"}", + "High School Mathematics - Observed inference time (s)": "{\"description\": \"min=0.234, mean=0.234, max=0.234, sum=0.468 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.2337868098859434\"}", + "High School Microeconomics - Observed inference time (s)": "{\"description\": \"min=0.184, mean=0.184, max=0.184, sum=0.367 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.18365505863638484\"}", + "High School Physics - Observed inference time (s)": "{\"description\": \"min=0.194, mean=0.194, max=0.194, sum=0.388 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.19382640068104726\"}", + "High School Psychology - Observed inference time (s)": "{\"description\": \"min=0.203, mean=0.203, max=0.203, sum=0.405 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.20258700432033713\"}", + "High School Statistics - Observed inference time (s)": "{\"description\": \"min=0.226, mean=0.226, max=0.226, sum=0.451 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.22551235446223505\"}", + "High School US History - Observed inference time (s)": "{\"description\": \"min=0.249, mean=0.249, max=0.249, sum=0.498 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.2492340417469249\"}", + "High School World History - Observed inference time (s)": "{\"description\": \"min=0.231, mean=0.231, max=0.231, sum=0.462 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.23088843812419393\"}", + "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=513.916, mean=513.916, max=513.916, sum=1027.832 (2)\", \"tab\": \"General information\", \"score\": \"513.916129032258\"}", + "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=517.261, mean=517.261, max=517.261, sum=1034.522 (2)\", \"tab\": \"General information\", \"score\": \"517.2610837438424\"}", + "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=878.46, mean=878.46, max=878.46, sum=1756.92 (2)\", \"tab\": \"General information\", \"score\": \"878.46\"}", + "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=2814.903, mean=2814.903, max=2814.903, sum=5629.806 (2)\", \"tab\": \"General information\", \"score\": \"2814.9030303030304\"}", + "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=372.217, mean=372.217, max=372.217, sum=744.434 (2)\", \"tab\": \"General information\", \"score\": \"372.2171717171717\"}", + "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=467.311, mean=467.311, max=467.311, sum=934.622 (2)\", \"tab\": \"General information\", \"score\": \"467.31088082901556\"}", + "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=374.349, mean=374.349, max=374.349, sum=748.697 (2)\", \"tab\": \"General information\", \"score\": \"374.34871794871793\"}", + "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=565.326, mean=565.326, max=565.326, sum=1130.652 (2)\", \"tab\": \"General information\", \"score\": \"565.325925925926\"}", + "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=402.277, mean=402.277, max=402.277, sum=804.555 (2)\", \"tab\": \"General information\", \"score\": \"402.2773109243698\"}", + "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=580.536, mean=580.536, max=580.536, sum=1161.073 (2)\", \"tab\": \"General information\", \"score\": \"580.5364238410596\"}", + "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=495.521, mean=495.521, max=495.521, sum=991.042 (2)\", \"tab\": \"General information\", \"score\": \"495.52110091743117\"}", + "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=830.477, mean=830.477, max=830.477, sum=1660.954 (2)\", \"tab\": \"General information\", \"score\": \"830.4768518518518\"}", + "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=2237.176, mean=2237.176, max=2237.176, sum=4474.353 (2)\", \"tab\": \"General information\", \"score\": \"2237.176470588235\"}", + "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=1448.354, mean=1448.354, max=1448.354, sum=2896.709 (2)\", \"tab\": \"General information\", \"score\": \"1448.3544303797469\"}", + "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": 0.794, + "details": { + "description": "min=0.794, mean=0.794, max=0.794, sum=1.588 (2)", + "tab": "Accuracy", + "Human Aging - Observed inference time (s)": "{\"description\": \"min=0.206, mean=0.206, max=0.206, sum=0.411 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.20559344591046663\"}", + "Human Sexuality - Observed inference time (s)": "{\"description\": \"min=0.191, mean=0.191, max=0.191, sum=0.381 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.19073554941716084\"}", + "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=322.121, mean=322.121, max=322.121, sum=644.242 (2)\", \"tab\": \"General information\", \"score\": \"322.1210762331838\"}", + "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=341.504, mean=341.504, max=341.504, sum=683.008 (2)\", \"tab\": \"General information\", \"score\": \"341.5038167938931\"}", + "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": 0.86, + "details": { + "description": "min=0.86, mean=0.86, max=0.86, sum=1.719 (2)", + "tab": "Accuracy", + "International Law - Observed inference time (s)": "{\"description\": \"min=0.23, mean=0.23, max=0.23, sum=0.46 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.22999596792804308\"}", + "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=640.579, mean=640.579, max=640.579, sum=1281.157 (2)\", \"tab\": \"General information\", \"score\": \"640.5785123966942\"}", + "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": 0.773, + "details": { + "description": "min=0.773, mean=0.773, max=0.773, sum=1.546 (2)", + "tab": "Accuracy", + "Logical Fallacies - Observed inference time (s)": "{\"description\": \"min=0.201, mean=0.201, max=0.201, sum=0.401 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.2005681289485627\"}", + "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=449.632, mean=449.632, max=449.632, sum=899.264 (2)\", \"tab\": \"General information\", \"score\": \"449.6319018404908\"}", + "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": 0.554, + "details": { + "description": "min=0.554, mean=0.554, max=0.554, sum=1.107 (2)", + "tab": "Accuracy", + "Machine Learning - Observed inference time (s)": "{\"description\": \"min=0.232, mean=0.232, max=0.232, sum=0.463 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.23156332118170603\"}", + "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=681.848, mean=681.848, max=681.848, sum=1363.696 (2)\", \"tab\": \"General information\", \"score\": \"681.8482142857143\"}", + "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": 0.845, + "details": { + "description": "min=0.845, mean=0.845, max=0.845, sum=1.689 (2)", + "tab": "Accuracy", + "Management - Observed inference time (s)": "{\"description\": \"min=0.197, mean=0.197, max=0.197, sum=0.394 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.19694008410555644\"}", + "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=283.854, mean=283.854, max=283.854, sum=567.709 (2)\", \"tab\": \"General information\", \"score\": \"283.8543689320388\"}", + "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": 0.919, + "details": { + "description": "min=0.919, mean=0.919, max=0.919, sum=1.838 (2)", + "tab": "Accuracy", + "Marketing - Observed inference time (s)": "{\"description\": \"min=0.184, mean=0.184, max=0.184, sum=0.368 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.18401269525544256\"}", + "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=404.415, mean=404.415, max=404.415, sum=808.829 (2)\", \"tab\": \"General information\", \"score\": \"404.4145299145299\"}", + "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": 0.85, + "details": { + "description": "min=0.85, mean=0.85, max=0.85, sum=1.7 (2)", + "tab": "Accuracy", + "Medical Genetics - Observed inference time (s)": "{\"description\": \"min=0.176, mean=0.176, max=0.176, sum=0.351 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.17553309679031373\"}", + "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=342.35, mean=342.35, max=342.35, sum=684.7 (2)\", \"tab\": \"General information\", \"score\": \"342.35\"}", + "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": 0.852, + "details": { + "description": "min=0.852, mean=0.852, max=0.852, sum=1.704 (2)", + "tab": "Accuracy", + "Miscellaneous - Observed inference time (s)": "{\"description\": \"min=0.174, mean=0.174, max=0.174, sum=0.347 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.17373346399377892\"}", + "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=303.7, mean=303.7, max=303.7, sum=607.4 (2)\", \"tab\": \"General information\", \"score\": \"303.6998722860792\"}", + "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": 0.511, + "details": { + "description": "min=0.511, mean=0.511, max=0.511, sum=1.021 (2)", + "tab": "Accuracy", + "Moral Disputes - Observed inference time (s)": "{\"description\": \"min=0.168, mean=0.168, max=0.168, sum=0.337 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.16836080041234894\"}", + "Moral Scenarios - Observed inference time (s)": "{\"description\": \"min=0.171, mean=0.171, max=0.171, sum=0.342 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.1708347949235799\"}", + "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=476.182, mean=476.182, max=476.182, sum=952.364 (2)\", \"tab\": \"General information\", \"score\": \"476.1820809248555\"}", + "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=668.494, mean=668.494, max=668.494, sum=1336.988 (2)\", \"tab\": \"General information\", \"score\": \"668.4938547486033\"}", + "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": 0.778, + "details": { + "description": "min=0.778, mean=0.778, max=0.778, sum=1.556 (2)", + "tab": "Accuracy", + "Nutrition - Observed inference time (s)": "{\"description\": \"min=0.168, mean=0.168, max=0.168, sum=0.337 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.16839487724054872\"}", + "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=599.637, mean=599.637, max=599.637, sum=1199.275 (2)\", \"tab\": \"General information\", \"score\": \"599.6372549019608\"}", + "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": 0.836, + "details": { + "description": "min=0.836, mean=0.836, max=0.836, sum=1.673 (2)", + "tab": "Accuracy", + "Prehistory - Observed inference time (s)": "{\"description\": \"min=0.168, mean=0.168, max=0.168, sum=0.337 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.16826030795956837\"}", + "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=528.364, mean=528.364, max=528.364, sum=1056.728 (2)\", \"tab\": \"General information\", \"score\": \"528.3641975308642\"}", + "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": 0.709, + "details": { + "description": "min=0.709, mean=0.709, max=0.709, sum=1.418 (2)", + "tab": "Accuracy", + "Public Relations - Observed inference time (s)": "{\"description\": \"min=0.164, mean=0.164, max=0.164, sum=0.328 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.1641989447853782\"}", + "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=408.427, mean=408.427, max=408.427, sum=816.855 (2)\", \"tab\": \"General information\", \"score\": \"408.42727272727274\"}", + "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": 0.682, + "details": { + "description": "min=0.682, mean=0.682, max=0.682, sum=1.363 (2)", + "tab": "Accuracy", + "Security Studies - Observed inference time (s)": "{\"description\": \"min=0.174, mean=0.174, max=0.174, sum=0.349 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.1744946577111069\"}", + "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=1166.931, mean=1166.931, max=1166.931, sum=2333.861 (2)\", \"tab\": \"General information\", \"score\": \"1166.930612244898\"}", + "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": 0.861, + "details": { + "description": "min=0.861, mean=0.861, max=0.861, sum=1.721 (2)", + "tab": "Accuracy", + "Sociology - Observed inference time (s)": "{\"description\": \"min=0.19, mean=0.19, max=0.19, sum=0.381 (2)\", \"tab\": \"Efficiency\", \"score\": \"0.1903395510431546\"}", + "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=450.1, mean=450.1, max=450.1, sum=900.199 (2)\", \"tab\": \"General information\", \"score\": \"450.0995024875622\"}", + "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 - 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Higher is better.", + "additional_details": { + "alphaxiv_y_axis": "RSUM Score", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Image-Text Retrieval on Flickr30k under Multimodal Co-Attack" + }, + "metric_id": "image_text_retrieval_on_flickr30k_under_multimodal_co_attack", + "metric_name": "Image-Text Retrieval on Flickr30k under Multimodal Co-Attack", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 210.2 + }, + "evaluation_result_id": "MMRobustness/ALBEF/1771591481.616601#mmrobustness#image_text_retrieval_on_flickr30k_under_multimodal_co_attack" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/87/64/87640848-3e10-4a72-856f-30df7e521927.json b/flat/objects/87/64/87640848-3e10-4a72-856f-30df7e521927.json new file mode 100644 index 0000000000000000000000000000000000000000..6f4dbaaa0b74bbe847be41143e4feed40ed3c99f --- /dev/null +++ b/flat/objects/87/64/87640848-3e10-4a72-856f-30df7e521927.json @@ -0,0 +1,208 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "WikiContradict/Mistral-7b-inst/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": "IBM Research – Thomas J. 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This evaluation was conducted on the full WikiContradict dataset of 253 instances.", + "additional_details": { + "alphaxiv_y_axis": "Correct Rate (%) - All Instances (Automated Eval)", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "WikiContradict: Automated Evaluation of LLM Responses to Contradictory Information (All Instances)" + }, + "metric_id": "wikicontradict_automated_evaluation_of_llm_responses_to_contradictory_information_all_instances", + "metric_name": "WikiContradict: Automated Evaluation of LLM Responses to Contradictory Information (All Instances)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 50.6 + }, + "evaluation_result_id": "WikiContradict/Mistral-7b-inst/1771591481.616601#wikicontradict#wikicontradict_automated_evaluation_of_llm_responses_to_contradictory_information_all_instances" + }, + { + "evaluation_name": "WikiContradict", + "source_data": { + "dataset_name": "WikiContradict", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2406.13805" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This metric shows the percentage of 'Correct' responses as judged by human annotators. LLMs were given two contradictory passages with an explicit instruction to provide a comprehensive answer that reflects the conflict (Prompt Template 5). A 'Correct' answer must identify and contain the contradictory information from both passages. 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Higher is better.", + "additional_details": { + "alphaxiv_y_axis": "Temporal Factuality (Avg) (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Temporal Factuality on TeCFaP (Zero-shot)" + }, + "metric_id": "temporal_factuality_on_tecfap_zero_shot", + "metric_name": "Temporal Factuality on TeCFaP (Zero-shot)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 2.99 + }, + "evaluation_result_id": "Temporally Consistent Factuality Probe/Falcon [7B]/1771591481.616601#temporally_consistent_factuality_probe#temporal_factuality_on_tecfap_zero_shot" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/87/76/877672c9-317a-49f4-abb9-08bdfc6401bf.json b/flat/objects/87/76/877672c9-317a-49f4-abb9-08bdfc6401bf.json new file mode 100644 index 0000000000000000000000000000000000000000..c6f3c9ee3dd11c7d5392fa1d16c33058a8aad691 --- /dev/null +++ b/flat/objects/87/76/877672c9-317a-49f4-abb9-08bdfc6401bf.json @@ -0,0 +1,118 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "FlashAdventure/Claude-3.7-Sonnet + Cradle (Self-grounded)/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": "Georgia Institute of Technology", + "alphaxiv_dataset_type": "image", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "Claude-3.7-Sonnet + Cradle (Self-grounded)", + "name": "Claude-3.7-Sonnet + Cradle (Self-grounded)", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "FlashAdventure", + "source_data": { + "dataset_name": "FlashAdventure", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2509.01052" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the average percentage of predefined milestones completed by GUI agents across all 34 games in the FlashAdventure benchmark. 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This is a binary metric per game, averaged across the entire benchmark. Higher scores are better. Most agents achieve a 0% success rate, highlighting the difficulty of the benchmark.", + "additional_details": { + "alphaxiv_y_axis": "Success Rate (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "FlashAdventure: Full Story Arc Success Rate" + }, + "metric_id": "flashadventure_full_story_arc_success_rate", + "metric_name": "FlashAdventure: Full Story Arc Success Rate", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0 + }, + "evaluation_result_id": "FlashAdventure/Claude-3.7-Sonnet + Cradle (Self-grounded)/1771591481.616601#flashadventure#flashadventure_full_story_arc_success_rate" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/87/7b/877bd834-7637-40d6-a05c-7ff6361706a8.json b/flat/objects/87/7b/877bd834-7637-40d6-a05c-7ff6361706a8.json new file mode 100644 index 0000000000000000000000000000000000000000..809ef2de59e8ad0ac8927a9e01aace2ba9420027 --- /dev/null +++ b/flat/objects/87/7b/877bd834-7637-40d6-a05c-7ff6361706a8.json @@ -0,0 +1,328 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "VSMB/VSMT (Swin-S)/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": "Beihang University", + "alphaxiv_dataset_type": "image", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "VSMT (Swin-S)", + "name": "VSMT (Swin-S)", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "VSMB", + "source_data": { + "dataset_name": "VSMB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2506.12105" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Higher Order Tracking Accuracy (HOTA) is a modern MOT metric that explicitly balances the effects of performing accurate detection, association, and localization. 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It is a component of the IDF1 score, focusing on minimizing missed identities. Results are from Table III on the Video SAR MOT Benchmark (VSMB). 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This metric reflects the tracker's ability to maintain long, consistent tracks. Results are from Table III on the Video SAR MOT Benchmark (VSMB). Higher values are better.", + "additional_details": { + "alphaxiv_y_axis": "MT", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Mostly Tracked (MT) Targets on VSMB" + }, + "metric_id": "mostly_tracked_mt_targets_on_vsmb", + "metric_name": "Mostly Tracked (MT) Targets on VSMB", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 243 + }, + "evaluation_result_id": "VSMB/VSMT (Swin-S)/1771591481.616601#vsmb#mostly_tracked_mt_targets_on_vsmb" + }, + { + "evaluation_name": "VSMB", + "source_data": { + "dataset_name": "VSMB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2506.12105" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Association Accuracy (AssA) is a component of the HOTA metric that measures the quality of identity association, independent of detection quality. It assesses the model's ability to maintain correct identities for detected targets. Results are from Table III on the Video SAR MOT Benchmark (VSMB). 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Higher values are better.", + "additional_details": { + "alphaxiv_y_axis": "Synonym Frequency - HM3D (Top 1, %)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Tiered Semantic Segmentation on HM3D (Top 1 Synonym Frequency)" + }, + "metric_id": "tiered_semantic_segmentation_on_hm3d_top_1_synonym_frequency", + "metric_name": "Tiered Semantic Segmentation on HM3D (Top 1 Synonym Frequency)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 8 + }, + "evaluation_result_id": "OpenLex3D/Kassab2024/1771591481.616601#openlex3d#tiered_semantic_segmentation_on_hm3d_top_1_synonym_frequency" + }, + { + "evaluation_name": "OpenLex3D", + "source_data": { + "dataset_name": "OpenLex3D", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.19764" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Supplementary evaluation on the Tiered Open-Set Semantic Segmentation task on the Replica dataset. 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Higher values are better.", + "additional_details": { + "alphaxiv_y_axis": "Synonym Frequency (Top 1, %)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Tiered Semantic Segmentation on Replica (Top 1 Synonym Frequency)" + }, + "metric_id": "tiered_semantic_segmentation_on_replica_top_1_synonym_frequency", + "metric_name": "Tiered Semantic Segmentation on Replica (Top 1 Synonym Frequency)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 13 + }, + "evaluation_result_id": "OpenLex3D/Kassab2024/1771591481.616601#openlex3d#tiered_semantic_segmentation_on_replica_top_1_synonym_frequency" + }, + { + "evaluation_name": "OpenLex3D", + "source_data": { + "dataset_name": "OpenLex3D", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.19764" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Supplementary evaluation on the Tiered Open-Set Semantic Segmentation task on the ScanNet++ dataset. 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The metric is Top-5 Synonym Frequency (F_S^5), measuring the proportion of 3D points whose top 5 predictions include a correct synonym label. Higher values are better.", + "additional_details": { + "alphaxiv_y_axis": "Synonym Frequency - HM3D (Top 5, %)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Tiered Semantic Segmentation on HM3D (Synonym Frequency)" + }, + "metric_id": "tiered_semantic_segmentation_on_hm3d_synonym_frequency", + "metric_name": "Tiered Semantic Segmentation on HM3D (Synonym Frequency)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 19 + }, + "evaluation_result_id": "OpenLex3D/Kassab2024/1771591481.616601#openlex3d#tiered_semantic_segmentation_on_hm3d_synonym_frequency" + }, + { + "evaluation_name": "OpenLex3D", + "source_data": { + "dataset_name": "OpenLex3D", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.19764" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Tiered Open-Set Semantic Segmentation task on the Replica dataset. The metric is Top-5 Synonym Frequency (F_S^5), measuring the proportion of 3D points whose top 5 predictions include a correct synonym label. Higher values are better.", + "additional_details": { + "alphaxiv_y_axis": "Synonym Frequency (Top 5, %)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Tiered Semantic Segmentation on Replica (Synonym Frequency)" + }, + "metric_id": "tiered_semantic_segmentation_on_replica_synonym_frequency", + "metric_name": "Tiered Semantic Segmentation on Replica (Synonym Frequency)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 26 + }, + "evaluation_result_id": "OpenLex3D/Kassab2024/1771591481.616601#openlex3d#tiered_semantic_segmentation_on_replica_synonym_frequency" + }, + { + "evaluation_name": "OpenLex3D", + "source_data": { + "dataset_name": "OpenLex3D", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.19764" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Open-Set Object Retrieval task on the HM3D dataset. 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Higher values are better.", + "additional_details": { + "alphaxiv_y_axis": "mAP - HM3D (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Open-Set Object Retrieval on HM3D (mAP)" + }, + "metric_id": "open_set_object_retrieval_on_hm3d_map", + "metric_name": "Open-Set Object Retrieval on HM3D (mAP)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 1.03 + }, + "evaluation_result_id": "OpenLex3D/Kassab2024/1771591481.616601#openlex3d#open_set_object_retrieval_on_hm3d_map" + }, + { + "evaluation_name": "OpenLex3D", + "source_data": { + "dataset_name": "OpenLex3D", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2503.19764" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Performance on the Tiered Open-Set Semantic Segmentation task on the ScanNet++ dataset. The metric is Top-5 Synonym Frequency (F_S^5), measuring the proportion of 3D points whose top 5 predictions include a correct synonym label. Higher values are better.", + "additional_details": { + "alphaxiv_y_axis": "Synonym Frequency - ScanNet++ (Top 5, %)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Tiered Semantic Segmentation on ScanNet++ (Synonym Frequency)" + }, + "metric_id": "tiered_semantic_segmentation_on_scannet_synonym_frequency", + "metric_name": "Tiered Semantic Segmentation on ScanNet++ (Synonym Frequency)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 11 + }, + "evaluation_result_id": "OpenLex3D/Kassab2024/1771591481.616601#openlex3d#tiered_semantic_segmentation_on_scannet_synonym_frequency" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/87/c1/87c16c74-321e-478c-826e-ae539c6d68e3.json b/flat/objects/87/c1/87c16c74-321e-478c-826e-ae539c6d68e3.json new file mode 100644 index 0000000000000000000000000000000000000000..64052896cfe855877d49f623d6a7fd0c7aceb319 --- /dev/null +++ b/flat/objects/87/c1/87c16c74-321e-478c-826e-ae539c6d68e3.json @@ -0,0 +1,231 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "helm_capabilities/anthropic_claude-haiku-4-5-20251001/1777589796.7306352", + "retrieved_timestamp": "1777589796.7306352", + "source_metadata": { + "source_name": "helm_capabilities", + "source_type": "documentation", + "source_organization_name": "crfm", + "evaluator_relationship": "third_party" + }, + "eval_library": { + "name": "helm", + "version": "unknown" + }, + "model_info": { + "name": "Claude 4.5 Haiku 20251001", + "id": "anthropic/claude-haiku-4-5-20251001", + "developer": "anthropic", + "inference_platform": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "Mean score", + "source_data": { + "dataset_name": "helm_capabilities", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/capabilities/benchmark_output/releases/v1.15.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "The mean of the scores from all columns.", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.717, + "details": { + "description": "", + "tab": "Accuracy", + "Mean score - 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The last line of your response should be of the form \"ANSWER: $ANSWER\" (without quotes) where $ANSWER is the answer to the problem.\n\n{prompt}\n\nRemember to put your answer on its own line at the end in the form \"ANSWER: $ANSWER\" (without quotes) where $ANSWER is the answer to the problem, and you do not need to use a \\boxed command.\n\nReasoning:", + "agentic_eval_config": { + "available_tools": [] + }, + "eval_plan": { + "name": "plan", + "steps": [ + "{\"solver\": \"system_message\", \"params\": {\"template\": \"Mimi picked up 2 dozen seashells on the beach. Kyle found twice as many shells as Mimi and put them in his pocket. Leigh grabbed one-third of the shells that Kyle found. How many seashells did Leigh have?\\n\\nReasoning:\\nMimi has 2 x 12 = <<2*12=24>>24 sea shells.\\nKyle has 24 x 2 = <<24*2=48>>48 sea shells.\\nLeigh has 48 / 3 = <<48/3=16>>16 sea shells.\\n\\nANSWER: 16\\n\\nFrankie's parents let him have many pets. He has six more snakes than he has cats. He has one less parrot than cats. Six of his pets have four legs. He has 2 dogs. How many pets does he have in total?\\n\\nReasoning:\\nHe has 6 - 2 = <<6-2=4>>4 cats.\\nHe has 4 - 1 = <<4-1=3>>3 parrots.\\nHe has 4 + 6 = <<4+6=10>>10 snakes.\\nHe has a total of 2 + 4 + 3 + 10 = <<2+4+3+10=19>>19 pets.\\n\\nANSWER: 19\\n\\nOlaf collects colorful toy cars. At first, his collection consisted of 150 cars. His family, knowing his hobby, decided to give him some toy cars. Grandpa gave Olaf twice as many toy cars as the uncle. Dad gave Olaf 10 toy cars, 5 less than Mum. Auntie gave Olaf 6 toy cars, 1 more than the uncle. How many toy cars does Olaf have in total, after receiving all these gifts?\\n\\nReasoning:\\nDad gave Olaf 10 toy cars,\\nMom has given Olaf 5 more toy cars than Dad, so 10 + 5 = <<10+5=15>>15 toy cars\\nAuntie gave Olaf 6 toy cars,\\nUncle has given 1 less toy than Auntie, so 6 - 1 = <<6-1=5>>5 toy cars\\nGrandpa gave Olaf 2 * 5 = <<2*5=10>>10 toy cars.\\nAll the family together gave Olaf 10 +15 + 6 + 5 + 10 = <<10+15+6+5+10=46>>46.\\nAdding the cars Olaf already had, Olaf's collection has 150 + 46 = <<150+46=196>>196 cars.\\n\\nANSWER: 196\\n\\nEmma's bank account has $100 in it. Each day of the week, she spends $8. At the end of the week, she goes to the bank and asks for as many $5 bills as her account can give her. She leaves the rest in the account. How many dollars remain in the account?\\n\\nReasoning:\\nShe spend $56 because 7 x 8 = <<7*8=56>>56\\nShe has $44 left in the bank because 100 - 56 = <<100-56=44>>44\\nShe can get 8 five dollar bills because 44 / 5 = <<44/5=8.8>>8.8\\nThis is equal to $40 because 8 x 5 = <<8*5=40>>40\\nShe has $4 left in the account because 44 - 40 = <<44-40=4>>4\\n\\nANSWER: 4\\n\\nEzekiel hikes as a hobby. This past summer, he did a challenging three-day hike across 50 kilometers of wilderness. The first day, he covered 10 kilometers of steep mountainside. The second day was flatter and he was able to cover half the full hike distance. How many kilometers did he have to hike on the third day to finish the hike?\\n\\nReasoning:\\nAfter the first day, Ezekiel had 50 - 10 = <<50-10=40>>40 kilometers of the hike left.\\nOn the second day, he covered 50 / 2 = <<50/2=25>>25 kilometers.\\nTherefore, on the third day, he had 40 - 25 = <<40-25=15>>15 kilometers left to finish the hike.\\n\\nANSWER: 15\\n\\nJames decides to build a tin house by collecting 500 tins in a week. On the first day, he collects 50 tins. On the second day, he manages to collect 3 times that number. On the third day, he collects 50 tins fewer than the number he collected on the second day. If he collects an equal number of tins on the remaining days of the week, what's the number of tins he collected each day for the rest of the week?\\n\\nReasoning:\\nOn the second day, he collected 3 times the number of tins he collected on the first day, which is 3*50 = <<3*50=150>>150 tins.\\nOn the third day, he collected 50 tins fewer than the second day, which is 150-50 = <<150-50=100>>100 tins\\nThe total for the three days is 150+100+50 = <<150+100+50=300>>300 tins.\\nTo reach his goal, he still needs 500-300 = <<500-300=200>>200 tins.\\nSince the total number of days left in the week is 4, he'll need to collect 200/4 = <<200/4=50>>50 tins per day to reach his goal\\n\\nANSWER: 50\\n\\nDon throws 3 darts. One is a bullseye worth 50 points. One completely missed the target, so received no points. The third was worth half the points of the bullseye. What is the final score from these 3 throws?\\n\\nReasoning:\\nThe third dart earned Don 50 / 2 = <<50/2=25>>25 points.\\nThus, his total score is 50 + 0 + 25 = <<50+0+25=75>>75 points.\\n\\nANSWER: 75\\n\\nTreQuan is throwing rocks in the river and he notices that the bigger the rock, the wider the splash. Pebbles make a splash that is a 1/4 meter wide. Rocks make a splash that is 1/2 a meter wide, and boulders create a splash that is 2 meters wide. If he tosses 6 pebbles, 3 rocks, and 2 boulders, what is the total width of the splashes he makes?\\n\\nReasoning:\\nThe pebble's total splash width is 1.5 meters because 6 times 1/4 equals <<6*1/4=1.5>>1.5.\\nThe rock's total splash width is 1.5 meters because 3 times 1/2 equals <<3*1/2=1.5>>1.5.\\nThe boulder's total splash width is 4 because 2 times 2 equals four.\\nThe total splash width for all the rocks is 7 because 1.5 plus 1.5 plus 4 equals 7.\\n\\nANSWER: 7\\n\\nPauly is making omelets for his family. There are three dozen eggs, and he plans to use them all. Each omelet requires 4 eggs. Including himself, there are 3 people. How many omelets does each person get?\\n\\nReasoning:\\nHe has 36 eggs because 3 x 12 = <<3*12=36>>36\\nHe can make 9 omelets because 36 / 4 = <<36/4=9>>9\\nEach person gets 3 omelets because 9 / 3 = <<9/3=3>>3\\n\\nANSWER: 3\\n\\nThomas made 4 stacks of wooden blocks. The first stack was 7 blocks tall. The second stack was 3 blocks taller than the first. The third stack was 6 blocks shorter than the second stack, and the fourth stack was 10 blocks taller than the third stack. If the fifth stack has twice as many blocks as the second stack, how many blocks did Thomas use in all?\\n\\nReasoning:\\nThe second stack has 7 blocks + 3 blocks = <<7+3=10>>10 blocks.\\nThe third stack has 10 blocks - 6 blocks = <<10-6=4>>4 blocks.\\nThe fourth stack has 4 blocks + 10 blocks = <<4+10=14>>14 blocks.\\nThe fifth stack has 10 blocks x 2 = <<10*2=20>>20 blocks.\\nIn total there are 7 blocks + 10 blocks + 4 blocks + 14 blocks + 20 blocks = <<7+10+4+14+20=55>>55 blocks.\\n\\nANSWER: 55\"}, \"params_passed\": {\"template\": \"Mimi picked up 2 dozen seashells on the beach. Kyle found twice as many shells as Mimi and put them in his pocket. Leigh grabbed one-third of the shells that Kyle found. How many seashells did Leigh have?\\n\\nReasoning:\\nMimi has 2 x 12 = <<2*12=24>>24 sea shells.\\nKyle has 24 x 2 = <<24*2=48>>48 sea shells.\\nLeigh has 48 / 3 = <<48/3=16>>16 sea shells.\\n\\nANSWER: 16\\n\\nFrankie's parents let him have many pets. He has six more snakes than he has cats. He has one less parrot than cats. Six of his pets have four legs. He has 2 dogs. How many pets does he have in total?\\n\\nReasoning:\\nHe has 6 - 2 = <<6-2=4>>4 cats.\\nHe has 4 - 1 = <<4-1=3>>3 parrots.\\nHe has 4 + 6 = <<4+6=10>>10 snakes.\\nHe has a total of 2 + 4 + 3 + 10 = <<2+4+3+10=19>>19 pets.\\n\\nANSWER: 19\\n\\nOlaf collects colorful toy cars. At first, his collection consisted of 150 cars. His family, knowing his hobby, decided to give him some toy cars. Grandpa gave Olaf twice as many toy cars as the uncle. Dad gave Olaf 10 toy cars, 5 less than Mum. Auntie gave Olaf 6 toy cars, 1 more than the uncle. How many toy cars does Olaf have in total, after receiving all these gifts?\\n\\nReasoning:\\nDad gave Olaf 10 toy cars,\\nMom has given Olaf 5 more toy cars than Dad, so 10 + 5 = <<10+5=15>>15 toy cars\\nAuntie gave Olaf 6 toy cars,\\nUncle has given 1 less toy than Auntie, so 6 - 1 = <<6-1=5>>5 toy cars\\nGrandpa gave Olaf 2 * 5 = <<2*5=10>>10 toy cars.\\nAll the family together gave Olaf 10 +15 + 6 + 5 + 10 = <<10+15+6+5+10=46>>46.\\nAdding the cars Olaf already had, Olaf's collection has 150 + 46 = <<150+46=196>>196 cars.\\n\\nANSWER: 196\\n\\nEmma's bank account has $100 in it. Each day of the week, she spends $8. At the end of the week, she goes to the bank and asks for as many $5 bills as her account can give her. She leaves the rest in the account. How many dollars remain in the account?\\n\\nReasoning:\\nShe spend $56 because 7 x 8 = <<7*8=56>>56\\nShe has $44 left in the bank because 100 - 56 = <<100-56=44>>44\\nShe can get 8 five dollar bills because 44 / 5 = <<44/5=8.8>>8.8\\nThis is equal to $40 because 8 x 5 = <<8*5=40>>40\\nShe has $4 left in the account because 44 - 40 = <<44-40=4>>4\\n\\nANSWER: 4\\n\\nEzekiel hikes as a hobby. This past summer, he did a challenging three-day hike across 50 kilometers of wilderness. The first day, he covered 10 kilometers of steep mountainside. The second day was flatter and he was able to cover half the full hike distance. How many kilometers did he have to hike on the third day to finish the hike?\\n\\nReasoning:\\nAfter the first day, Ezekiel had 50 - 10 = <<50-10=40>>40 kilometers of the hike left.\\nOn the second day, he covered 50 / 2 = <<50/2=25>>25 kilometers.\\nTherefore, on the third day, he had 40 - 25 = <<40-25=15>>15 kilometers left to finish the hike.\\n\\nANSWER: 15\\n\\nJames decides to build a tin house by collecting 500 tins in a week. On the first day, he collects 50 tins. On the second day, he manages to collect 3 times that number. On the third day, he collects 50 tins fewer than the number he collected on the second day. If he collects an equal number of tins on the remaining days of the week, what's the number of tins he collected each day for the rest of the week?\\n\\nReasoning:\\nOn the second day, he collected 3 times the number of tins he collected on the first day, which is 3*50 = <<3*50=150>>150 tins.\\nOn the third day, he collected 50 tins fewer than the second day, which is 150-50 = <<150-50=100>>100 tins\\nThe total for the three days is 150+100+50 = <<150+100+50=300>>300 tins.\\nTo reach his goal, he still needs 500-300 = <<500-300=200>>200 tins.\\nSince the total number of days left in the week is 4, he'll need to collect 200/4 = <<200/4=50>>50 tins per day to reach his goal\\n\\nANSWER: 50\\n\\nDon throws 3 darts. One is a bullseye worth 50 points. One completely missed the target, so received no points. The third was worth half the points of the bullseye. What is the final score from these 3 throws?\\n\\nReasoning:\\nThe third dart earned Don 50 / 2 = <<50/2=25>>25 points.\\nThus, his total score is 50 + 0 + 25 = <<50+0+25=75>>75 points.\\n\\nANSWER: 75\\n\\nTreQuan is throwing rocks in the river and he notices that the bigger the rock, the wider the splash. Pebbles make a splash that is a 1/4 meter wide. Rocks make a splash that is 1/2 a meter wide, and boulders create a splash that is 2 meters wide. If he tosses 6 pebbles, 3 rocks, and 2 boulders, what is the total width of the splashes he makes?\\n\\nReasoning:\\nThe pebble's total splash width is 1.5 meters because 6 times 1/4 equals <<6*1/4=1.5>>1.5.\\nThe rock's total splash width is 1.5 meters because 3 times 1/2 equals <<3*1/2=1.5>>1.5.\\nThe boulder's total splash width is 4 because 2 times 2 equals four.\\nThe total splash width for all the rocks is 7 because 1.5 plus 1.5 plus 4 equals 7.\\n\\nANSWER: 7\\n\\nPauly is making omelets for his family. There are three dozen eggs, and he plans to use them all. Each omelet requires 4 eggs. Including himself, there are 3 people. How many omelets does each person get?\\n\\nReasoning:\\nHe has 36 eggs because 3 x 12 = <<3*12=36>>36\\nHe can make 9 omelets because 36 / 4 = <<36/4=9>>9\\nEach person gets 3 omelets because 9 / 3 = <<9/3=3>>3\\n\\nANSWER: 3\\n\\nThomas made 4 stacks of wooden blocks. The first stack was 7 blocks tall. The second stack was 3 blocks taller than the first. The third stack was 6 blocks shorter than the second stack, and the fourth stack was 10 blocks taller than the third stack. If the fifth stack has twice as many blocks as the second stack, how many blocks did Thomas use in all?\\n\\nReasoning:\\nThe second stack has 7 blocks + 3 blocks = <<7+3=10>>10 blocks.\\nThe third stack has 10 blocks - 6 blocks = <<10-6=4>>4 blocks.\\nThe fourth stack has 4 blocks + 10 blocks = <<4+10=14>>14 blocks.\\nThe fifth stack has 10 blocks x 2 = <<10*2=20>>20 blocks.\\nIn total there are 7 blocks + 10 blocks + 4 blocks + 14 blocks + 20 blocks = <<7+10+4+14+20=55>>55 blocks.\\n\\nANSWER: 55\"}}", + "{\"solver\": \"prompt_template\", \"params\": {\"template\": \"Solve the following math problem step by step. 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A lower IMR indicates better performance in identifying fake news.", + "additional_details": { + "alphaxiv_y_axis": "IMR (%) - Fake News", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Insight Mastery Rate on Fake News (IMR)" + }, + "metric_id": "insight_mastery_rate_on_fake_news_imr", + "metric_name": "Insight Mastery Rate on Fake News (IMR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 33.75 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#insight_mastery_rate_on_fake_news_imr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the overall proportion of low-scoring fact-checking responses (Grade ≤ 3.0) across all tasks. 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A lower IMR indicates better performance.", + "additional_details": { + "alphaxiv_y_axis": "IMR (%) - Overall", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Overall Insight Mastery Rate (IMR)" + }, + "metric_id": "overall_insight_mastery_rate_imr", + "metric_name": "Overall Insight Mastery Rate (IMR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 38.67 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#overall_insight_mastery_rate_imr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the proportion of low-scoring fact-checking responses (Grade ≤ 3.0) on the 'Social Rumor' task. A lower IMR indicates better performance in debunking social rumors.", + "additional_details": { + "alphaxiv_y_axis": "IMR (%) - Social Rumor", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Insight Mastery Rate on Social Rumors (IMR)" + }, + "metric_id": "insight_mastery_rate_on_social_rumors_imr", + "metric_name": "Insight Mastery Rate on Social Rumors (IMR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 46.25 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#insight_mastery_rate_on_social_rumors_imr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the percentage of cases with a correct verdict but a poor justification on the 'Complex Claim' task. 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A lower JFR indicates better justification quality when identifying fake news.", + "additional_details": { + "alphaxiv_y_axis": "JFR (%) - Fake News", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Justification Flaw Rate on Fake News (JFR)" + }, + "metric_id": "justification_flaw_rate_on_fake_news_jfr", + "metric_name": "Justification Flaw Rate on Fake News (JFR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 17.28 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#justification_flaw_rate_on_fake_news_jfr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This supplementary metric measures the ratio of justification flaws among all poorly-performing cases for the 'Complex Claim' task. A high ratio suggests that when a model fails on complex claims, it is frequently due to a poor justification. A higher score indicates a greater tendency to produce flawed justifications.", + "additional_details": { + "alphaxiv_y_axis": "JFR/IMR Ratio (%) - Complex Claim", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Ratio of Justification Flaws in Bad Cases for Complex Claims (JFR/IMR)" + }, + "metric_id": "ratio_of_justification_flaws_in_bad_cases_for_complex_claims_jfr_imr", + "metric_name": "Ratio of Justification Flaws in Bad Cases for Complex Claims (JFR/IMR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 30.37 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#ratio_of_justification_flaws_in_bad_cases_for_complex_claims_jfr_imr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This supplementary metric measures the ratio of justification flaws among all poorly-performing cases for the 'Fake News' task. A high ratio suggests that when a model fails on fake news, it is frequently due to a poor justification. A higher score indicates a greater tendency to produce flawed justifications.", + "additional_details": { + "alphaxiv_y_axis": "JFR/IMR Ratio (%) - Fake News", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Ratio of Justification Flaws in Bad Cases for Fake News (JFR/IMR)" + }, + "metric_id": "ratio_of_justification_flaws_in_bad_cases_for_fake_news_jfr_imr", + "metric_name": "Ratio of Justification Flaws in Bad Cases for Fake News (JFR/IMR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 51.23 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#ratio_of_justification_flaws_in_bad_cases_for_fake_news_jfr_imr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This supplementary metric measures the ratio of justification flaws among all poorly-performing cases (those with a low grade). A high ratio suggests that when a model fails, it is frequently due to a poor justification, even if the verdict is correct. A higher score indicates a greater tendency to produce flawed justifications in challenging scenarios.", + "additional_details": { + "alphaxiv_y_axis": "JFR/IMR Ratio (%) - Overall", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Overall Ratio of Justification Flaws in Bad Cases (JFR/IMR)" + }, + "metric_id": "overall_ratio_of_justification_flaws_in_bad_cases_jfr_imr", + "metric_name": "Overall Ratio of Justification Flaws in Bad Cases (JFR/IMR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 40.3 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#overall_ratio_of_justification_flaws_in_bad_cases_jfr_imr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "This supplementary metric measures the ratio of justification flaws among all poorly-performing cases for the 'Social Rumor' task. A high ratio suggests that when a model fails on social rumors, it is frequently due to a poor justification. A higher score indicates a greater tendency to produce flawed justifications.", + "additional_details": { + "alphaxiv_y_axis": "JFR/IMR Ratio (%) - Social Rumor", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Ratio of Justification Flaws in Bad Cases for Social Rumors (JFR/IMR)" + }, + "metric_id": "ratio_of_justification_flaws_in_bad_cases_for_social_rumors_jfr_imr", + "metric_name": "Ratio of Justification Flaws in Bad Cases for Social Rumors (JFR/IMR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 41.44 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#ratio_of_justification_flaws_in_bad_cases_for_social_rumors_jfr_imr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the overall percentage of cases where the LLM provided a correct verdict but a poor justification. This metric specifically isolates failures in reasoning and explanation. A lower JFR indicates better justification quality.", + "additional_details": { + "alphaxiv_y_axis": "JFR (%) - Overall", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Overall Justification Flaw Rate (JFR)" + }, + "metric_id": "overall_justification_flaw_rate_jfr", + "metric_name": "Overall Justification Flaw Rate (JFR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 15.6 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#overall_justification_flaw_rate_jfr" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates the quality of an LLM's fact-checking response (verdict and justification) specifically on the 'Complex Claim' task. This task requires advanced reasoning over nuanced or multi-faceted claims. A higher grade indicates better performance.", + "additional_details": { + "alphaxiv_y_axis": "Grade (Complex Claim)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Fact-Checking Performance on Complex Claims (Grade)" + }, + "metric_id": "fact_checking_performance_on_complex_claims_grade", + "metric_name": "Fact-Checking Performance on Complex Claims (Grade)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 5.19 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#fact_checking_performance_on_complex_claims_grade" + }, + { + "evaluation_name": "FACT-AUDIT", + "source_data": { + "dataset_name": "FACT-AUDIT", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.17924" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the percentage of cases with a correct verdict but a poor justification on the 'Social Rumor' task. A lower JFR indicates better justification quality when analyzing social rumors.", + "additional_details": { + "alphaxiv_y_axis": "JFR (%) - Social Rumor", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Justification Flaw Rate on Social Rumors (JFR)" + }, + "metric_id": "justification_flaw_rate_on_social_rumors_jfr", + "metric_name": "Justification Flaw Rate on Social Rumors (JFR)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 19.18 + }, + "evaluation_result_id": "FACT-AUDIT/Llama3-8B/1771591481.616601#fact_audit#justification_flaw_rate_on_social_rumors_jfr" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +}