| { | |
| "results": { | |
| "repellendus-laborum_lsat-rc_base": { | |
| "acc,none": 0.26765799256505574, | |
| "acc_stderr,none": 0.0270445453145873, | |
| "alias": "repellendus-laborum_lsat-rc_base" | |
| }, | |
| "repellendus-laborum_lsat-lr_base": { | |
| "acc,none": 0.23137254901960785, | |
| "acc_stderr,none": 0.018691965462419517, | |
| "alias": "repellendus-laborum_lsat-lr_base" | |
| }, | |
| "repellendus-laborum_lsat-ar_base": { | |
| "acc,none": 0.25217391304347825, | |
| "acc_stderr,none": 0.028696745294493366, | |
| "alias": "repellendus-laborum_lsat-ar_base" | |
| }, | |
| "repellendus-laborum_logiqa_base": { | |
| "acc,none": 0.29073482428115016, | |
| "acc_stderr,none": 0.0181640562091778, | |
| "alias": "repellendus-laborum_logiqa_base" | |
| }, | |
| "repellendus-laborum_logiqa2_base": { | |
| "acc,none": 0.29389312977099236, | |
| "acc_stderr,none": 0.011493223255677107, | |
| "alias": "repellendus-laborum_logiqa2_base" | |
| }, | |
| "possimus-voluptate_lsat-rc_base": { | |
| "acc,none": 0.2527881040892193, | |
| "acc_stderr,none": 0.026548061072649957, | |
| "alias": "possimus-voluptate_lsat-rc_base" | |
| }, | |
| "possimus-voluptate_lsat-lr_base": { | |
| "acc,none": 0.22941176470588234, | |
| "acc_stderr,none": 0.01863631913244453, | |
| "alias": "possimus-voluptate_lsat-lr_base" | |
| }, | |
| "possimus-voluptate_lsat-ar_base": { | |
| "acc,none": 0.21739130434782608, | |
| "acc_stderr,none": 0.02725685083881996, | |
| "alias": "possimus-voluptate_lsat-ar_base" | |
| }, | |
| "possimus-voluptate_logiqa_base": { | |
| "acc,none": 0.2795527156549521, | |
| "acc_stderr,none": 0.017951178003680606, | |
| "alias": "possimus-voluptate_logiqa_base" | |
| }, | |
| "possimus-voluptate_logiqa2_base": { | |
| "acc,none": 0.2926208651399491, | |
| "acc_stderr,none": 0.01147864633663911, | |
| "alias": "possimus-voluptate_logiqa2_base" | |
| }, | |
| "maxime-expedita_lsat-rc_base": { | |
| "acc,none": 0.2527881040892193, | |
| "acc_stderr,none": 0.026548061072649953, | |
| "alias": "maxime-expedita_lsat-rc_base" | |
| }, | |
| "maxime-expedita_lsat-lr_base": { | |
| "acc,none": 0.24705882352941178, | |
| "acc_stderr,none": 0.019117091440867724, | |
| "alias": "maxime-expedita_lsat-lr_base" | |
| }, | |
| "maxime-expedita_lsat-ar_base": { | |
| "acc,none": 0.23478260869565218, | |
| "acc_stderr,none": 0.028009647070930132, | |
| "alias": "maxime-expedita_lsat-ar_base" | |
| }, | |
| "maxime-expedita_logiqa_base": { | |
| "acc,none": 0.2971246006389776, | |
| "acc_stderr,none": 0.018279674935144995, | |
| "alias": "maxime-expedita_logiqa_base" | |
| }, | |
| "maxime-expedita_logiqa2_base": { | |
| "acc,none": 0.27099236641221375, | |
| "acc_stderr,none": 0.011213894711527516, | |
| "alias": "maxime-expedita_logiqa2_base" | |
| }, | |
| "eveniet-ea_lsat-rc_base": { | |
| "acc,none": 0.2899628252788104, | |
| "acc_stderr,none": 0.027716877855226897, | |
| "alias": "eveniet-ea_lsat-rc_base" | |
| }, | |
| "eveniet-ea_lsat-lr_base": { | |
| "acc,none": 0.23333333333333334, | |
| "acc_stderr,none": 0.01874704371659074, | |
| "alias": "eveniet-ea_lsat-lr_base" | |
| }, | |
| "eveniet-ea_lsat-ar_base": { | |
| "acc,none": 0.2217391304347826, | |
| "acc_stderr,none": 0.02745149660405891, | |
| "alias": "eveniet-ea_lsat-ar_base" | |
| }, | |
| "eveniet-ea_logiqa_base": { | |
| "acc,none": 0.2955271565495208, | |
| "acc_stderr,none": 0.018251174484565112, | |
| "alias": "eveniet-ea_logiqa_base" | |
| }, | |
| "eveniet-ea_logiqa2_base": { | |
| "acc,none": 0.2837150127226463, | |
| "acc_stderr,none": 0.011373548669758796, | |
| "alias": "eveniet-ea_logiqa2_base" | |
| }, | |
| "distinctio-unde_lsat-rc_base": { | |
| "acc,none": 0.26394052044609667, | |
| "acc_stderr,none": 0.026924155643902548, | |
| "alias": "distinctio-unde_lsat-rc_base" | |
| }, | |
| "distinctio-unde_lsat-lr_base": { | |
| "acc,none": 0.2647058823529412, | |
| "acc_stderr,none": 0.019554803257850088, | |
| "alias": "distinctio-unde_lsat-lr_base" | |
| }, | |
| "distinctio-unde_lsat-ar_base": { | |
| "acc,none": 0.24347826086956523, | |
| "acc_stderr,none": 0.028361099300075063, | |
| "alias": "distinctio-unde_lsat-ar_base" | |
| }, | |
| "distinctio-unde_logiqa_base": { | |
| "acc,none": 0.3003194888178914, | |
| "acc_stderr,none": 0.01833587493212361, | |
| "alias": "distinctio-unde_logiqa_base" | |
| }, | |
| "distinctio-unde_logiqa2_base": { | |
| "acc,none": 0.2970737913486005, | |
| "acc_stderr,none": 0.011529193947365896, | |
| "alias": "distinctio-unde_logiqa2_base" | |
| }, | |
| "aspernatur-sint_lsat-rc_base": { | |
| "acc,none": 0.26765799256505574, | |
| "acc_stderr,none": 0.027044545314587293, | |
| "alias": "aspernatur-sint_lsat-rc_base" | |
| }, | |
| "aspernatur-sint_lsat-lr_base": { | |
| "acc,none": 0.27058823529411763, | |
| "acc_stderr,none": 0.0196916426487322, | |
| "alias": "aspernatur-sint_lsat-lr_base" | |
| }, | |
| "aspernatur-sint_lsat-ar_base": { | |
| "acc,none": 0.2217391304347826, | |
| "acc_stderr,none": 0.027451496604058913, | |
| "alias": "aspernatur-sint_lsat-ar_base" | |
| }, | |
| "aspernatur-sint_logiqa_base": { | |
| "acc,none": 0.2955271565495208, | |
| "acc_stderr,none": 0.018251174484565112, | |
| "alias": "aspernatur-sint_logiqa_base" | |
| }, | |
| "aspernatur-sint_logiqa2_base": { | |
| "acc,none": 0.2868956743002545, | |
| "acc_stderr,none": 0.01141170254782954, | |
| "alias": "aspernatur-sint_logiqa2_base" | |
| } | |
| }, | |
| "configs": { | |
| "aspernatur-sint_logiqa2_base": { | |
| "task": "aspernatur-sint_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "aspernatur-sint-logiqa2/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "aspernatur-sint_logiqa_base": { | |
| "task": "aspernatur-sint_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "aspernatur-sint-logiqa/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "aspernatur-sint_lsat-ar_base": { | |
| "task": "aspernatur-sint_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "aspernatur-sint-lsat-ar/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "aspernatur-sint_lsat-lr_base": { | |
| "task": "aspernatur-sint_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "aspernatur-sint-lsat-lr/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "aspernatur-sint_lsat-rc_base": { | |
| "task": "aspernatur-sint_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "aspernatur-sint-lsat-rc/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "distinctio-unde_logiqa2_base": { | |
| "task": "distinctio-unde_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "distinctio-unde-logiqa2/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "distinctio-unde_logiqa_base": { | |
| "task": "distinctio-unde_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "distinctio-unde-logiqa/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "distinctio-unde_lsat-ar_base": { | |
| "task": "distinctio-unde_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "distinctio-unde-lsat-ar/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "distinctio-unde_lsat-lr_base": { | |
| "task": "distinctio-unde_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "distinctio-unde-lsat-lr/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "distinctio-unde_lsat-rc_base": { | |
| "task": "distinctio-unde_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "distinctio-unde-lsat-rc/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "eveniet-ea_logiqa2_base": { | |
| "task": "eveniet-ea_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "eveniet-ea-logiqa2/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "eveniet-ea_logiqa_base": { | |
| "task": "eveniet-ea_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "eveniet-ea-logiqa/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "eveniet-ea_lsat-ar_base": { | |
| "task": "eveniet-ea_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "eveniet-ea-lsat-ar/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "eveniet-ea_lsat-lr_base": { | |
| "task": "eveniet-ea_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "eveniet-ea-lsat-lr/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "eveniet-ea_lsat-rc_base": { | |
| "task": "eveniet-ea_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "eveniet-ea-lsat-rc/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "maxime-expedita_logiqa2_base": { | |
| "task": "maxime-expedita_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "maxime-expedita-logiqa2/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "maxime-expedita_logiqa_base": { | |
| "task": "maxime-expedita_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "maxime-expedita-logiqa/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "maxime-expedita_lsat-ar_base": { | |
| "task": "maxime-expedita_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "maxime-expedita-lsat-ar/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "maxime-expedita_lsat-lr_base": { | |
| "task": "maxime-expedita_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "maxime-expedita-lsat-lr/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "maxime-expedita_lsat-rc_base": { | |
| "task": "maxime-expedita_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "maxime-expedita-lsat-rc/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "possimus-voluptate_logiqa2_base": { | |
| "task": "possimus-voluptate_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "possimus-voluptate-logiqa2/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "possimus-voluptate_logiqa_base": { | |
| "task": "possimus-voluptate_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "possimus-voluptate-logiqa/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "possimus-voluptate_lsat-ar_base": { | |
| "task": "possimus-voluptate_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "possimus-voluptate-lsat-ar/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "possimus-voluptate_lsat-lr_base": { | |
| "task": "possimus-voluptate_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "possimus-voluptate-lsat-lr/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "possimus-voluptate_lsat-rc_base": { | |
| "task": "possimus-voluptate_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "possimus-voluptate-lsat-rc/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "repellendus-laborum_logiqa2_base": { | |
| "task": "repellendus-laborum_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "repellendus-laborum-logiqa2/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "repellendus-laborum_logiqa_base": { | |
| "task": "repellendus-laborum_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "repellendus-laborum-logiqa/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "repellendus-laborum_lsat-ar_base": { | |
| "task": "repellendus-laborum_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "repellendus-laborum-lsat-ar/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "repellendus-laborum_lsat-lr_base": { | |
| "task": "repellendus-laborum_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "repellendus-laborum-lsat-lr/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "repellendus-laborum_lsat-rc_base": { | |
| "task": "repellendus-laborum_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "repellendus-laborum-lsat-rc/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
| "aspernatur-sint_logiqa2_base": 0.0, | |
| "aspernatur-sint_logiqa_base": 0.0, | |
| "aspernatur-sint_lsat-ar_base": 0.0, | |
| "aspernatur-sint_lsat-lr_base": 0.0, | |
| "aspernatur-sint_lsat-rc_base": 0.0, | |
| "distinctio-unde_logiqa2_base": 0.0, | |
| "distinctio-unde_logiqa_base": 0.0, | |
| "distinctio-unde_lsat-ar_base": 0.0, | |
| "distinctio-unde_lsat-lr_base": 0.0, | |
| "distinctio-unde_lsat-rc_base": 0.0, | |
| "eveniet-ea_logiqa2_base": 0.0, | |
| "eveniet-ea_logiqa_base": 0.0, | |
| "eveniet-ea_lsat-ar_base": 0.0, | |
| "eveniet-ea_lsat-lr_base": 0.0, | |
| "eveniet-ea_lsat-rc_base": 0.0, | |
| "maxime-expedita_logiqa2_base": 0.0, | |
| "maxime-expedita_logiqa_base": 0.0, | |
| "maxime-expedita_lsat-ar_base": 0.0, | |
| "maxime-expedita_lsat-lr_base": 0.0, | |
| "maxime-expedita_lsat-rc_base": 0.0, | |
| "possimus-voluptate_logiqa2_base": 0.0, | |
| "possimus-voluptate_logiqa_base": 0.0, | |
| "possimus-voluptate_lsat-ar_base": 0.0, | |
| "possimus-voluptate_lsat-lr_base": 0.0, | |
| "possimus-voluptate_lsat-rc_base": 0.0, | |
| "repellendus-laborum_logiqa2_base": 0.0, | |
| "repellendus-laborum_logiqa_base": 0.0, | |
| "repellendus-laborum_lsat-ar_base": 0.0, | |
| "repellendus-laborum_lsat-lr_base": 0.0, | |
| "repellendus-laborum_lsat-rc_base": 0.0 | |
| }, | |
| "n-shot": { | |
| "aspernatur-sint_logiqa2_base": 0, | |
| "aspernatur-sint_logiqa_base": 0, | |
| "aspernatur-sint_lsat-ar_base": 0, | |
| "aspernatur-sint_lsat-lr_base": 0, | |
| "aspernatur-sint_lsat-rc_base": 0, | |
| "distinctio-unde_logiqa2_base": 0, | |
| "distinctio-unde_logiqa_base": 0, | |
| "distinctio-unde_lsat-ar_base": 0, | |
| "distinctio-unde_lsat-lr_base": 0, | |
| "distinctio-unde_lsat-rc_base": 0, | |
| "eveniet-ea_logiqa2_base": 0, | |
| "eveniet-ea_logiqa_base": 0, | |
| "eveniet-ea_lsat-ar_base": 0, | |
| "eveniet-ea_lsat-lr_base": 0, | |
| "eveniet-ea_lsat-rc_base": 0, | |
| "maxime-expedita_logiqa2_base": 0, | |
| "maxime-expedita_logiqa_base": 0, | |
| "maxime-expedita_lsat-ar_base": 0, | |
| "maxime-expedita_lsat-lr_base": 0, | |
| "maxime-expedita_lsat-rc_base": 0, | |
| "possimus-voluptate_logiqa2_base": 0, | |
| "possimus-voluptate_logiqa_base": 0, | |
| "possimus-voluptate_lsat-ar_base": 0, | |
| "possimus-voluptate_lsat-lr_base": 0, | |
| "possimus-voluptate_lsat-rc_base": 0, | |
| "repellendus-laborum_logiqa2_base": 0, | |
| "repellendus-laborum_logiqa_base": 0, | |
| "repellendus-laborum_lsat-ar_base": 0, | |
| "repellendus-laborum_lsat-lr_base": 0, | |
| "repellendus-laborum_lsat-rc_base": 0 | |
| }, | |
| "config": { | |
| "model": "vllm", | |
| "model_args": "pretrained=microsoft/phi-2,revision=main,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.9,trust_remote_code=true,max_length=2048", | |
| "batch_size": "auto", | |
| "batch_sizes": [], | |
| "device": null, | |
| "use_cache": null, | |
| "limit": null, | |
| "bootstrap_iters": 100000, | |
| "gen_kwargs": null | |
| }, | |
| "git_hash": "3d5b980" | |
| } |