{ "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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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" }