{ "results": { "omnis-voluptatibus_lsat-rc_cot": { "acc,none": 0.26394052044609667, "acc_stderr,none": 0.026924155643902548, "alias": "omnis-voluptatibus_lsat-rc_cot" }, "omnis-voluptatibus_lsat-lr_cot": { "acc,none": 0.2196078431372549, "acc_stderr,none": 0.018349383611423235, "alias": "omnis-voluptatibus_lsat-lr_cot" }, "omnis-voluptatibus_lsat-ar_cot": { "acc,none": 0.21739130434782608, "acc_stderr,none": 0.027256850838819964, "alias": "omnis-voluptatibus_lsat-ar_cot" }, "omnis-voluptatibus_logiqa_cot": { "acc,none": 0.29073482428115016, "acc_stderr,none": 0.018164056209177798, "alias": "omnis-voluptatibus_logiqa_cot" }, "omnis-voluptatibus_logiqa2_cot": { "acc,none": 0.29834605597964375, "acc_stderr,none": 0.011543394639779811, "alias": "omnis-voluptatibus_logiqa2_cot" }, "magnam-eius_lsat-rc_cot": { "acc,none": 0.27137546468401486, "acc_stderr,none": 0.027162503089239516, "alias": "magnam-eius_lsat-rc_cot" }, "magnam-eius_lsat-lr_cot": { "acc,none": 0.2196078431372549, "acc_stderr,none": 0.018349383611423218, "alias": "magnam-eius_lsat-lr_cot" }, "magnam-eius_lsat-ar_cot": { "acc,none": 0.2, "acc_stderr,none": 0.02643274401820356, "alias": "magnam-eius_lsat-ar_cot" }, "magnam-eius_logiqa_cot": { "acc,none": 0.29073482428115016, "acc_stderr,none": 0.018164056209177805, "alias": "magnam-eius_logiqa_cot" }, "magnam-eius_logiqa2_cot": { "acc,none": 0.3142493638676845, "acc_stderr,none": 0.011712031200512734, "alias": "magnam-eius_logiqa2_cot" }, "libero-exercitationem_lsat-rc_cot": { "acc,none": 0.26022304832713755, "acc_stderr,none": 0.02680130130545777, "alias": "libero-exercitationem_lsat-rc_cot" }, "libero-exercitationem_lsat-lr_cot": { "acc,none": 0.20980392156862746, "acc_stderr,none": 0.018047429112476115, "alias": "libero-exercitationem_lsat-lr_cot" }, "libero-exercitationem_lsat-ar_cot": { "acc,none": 0.24347826086956523, "acc_stderr,none": 0.02836109930007507, "alias": "libero-exercitationem_lsat-ar_cot" }, "libero-exercitationem_logiqa_cot": { "acc,none": 0.3210862619808307, "acc_stderr,none": 0.018675754307572432, "alias": "libero-exercitationem_logiqa_cot" }, "libero-exercitationem_logiqa2_cot": { "acc,none": 0.30916030534351147, "acc_stderr,none": 0.0116598352236769, "alias": "libero-exercitationem_logiqa2_cot" }, "illum-eaque_lsat-rc_cot": { "acc,none": 0.26765799256505574, "acc_stderr,none": 0.027044545314587293, "alias": "illum-eaque_lsat-rc_cot" }, "illum-eaque_lsat-lr_cot": { "acc,none": 0.23137254901960785, "acc_stderr,none": 0.018691965462419545, "alias": "illum-eaque_lsat-lr_cot" }, "illum-eaque_lsat-ar_cot": { "acc,none": 0.2, "acc_stderr,none": 0.026432744018203558, "alias": "illum-eaque_lsat-ar_cot" }, "illum-eaque_logiqa_cot": { "acc,none": 0.29233226837060705, "acc_stderr,none": 0.018193366406024092, "alias": "illum-eaque_logiqa_cot" }, "illum-eaque_logiqa2_cot": { "acc,none": 0.294529262086514, "acc_stderr,none": 0.011500471190116962, "alias": "illum-eaque_logiqa2_cot" }, "amet-ullam_lsat-rc_cot": { "acc,none": 0.2788104089219331, "acc_stderr,none": 0.02739124797571039, "alias": "amet-ullam_lsat-rc_cot" }, "amet-ullam_lsat-lr_cot": { "acc,none": 0.20980392156862746, "acc_stderr,none": 0.018047429112476105, "alias": "amet-ullam_lsat-lr_cot" }, "amet-ullam_lsat-ar_cot": { "acc,none": 0.2, "acc_stderr,none": 0.02643274401820355, "alias": "amet-ullam_lsat-ar_cot" }, "amet-ullam_logiqa_cot": { "acc,none": 0.29233226837060705, "acc_stderr,none": 0.018193366406024095, "alias": "amet-ullam_logiqa_cot" }, "amet-ullam_logiqa2_cot": { "acc,none": 0.2951653944020356, "acc_stderr,none": 0.011507692175964774, "alias": "amet-ullam_logiqa2_cot" }, "accusantium-inventore_lsat-rc_cot": { "acc,none": 0.26765799256505574, "acc_stderr,none": 0.027044545314587293, "alias": "accusantium-inventore_lsat-rc_cot" }, "accusantium-inventore_lsat-lr_cot": { "acc,none": 0.2, "acc_stderr,none": 0.01772968828711749, "alias": "accusantium-inventore_lsat-lr_cot" }, "accusantium-inventore_lsat-ar_cot": { "acc,none": 0.20869565217391303, "acc_stderr,none": 0.026854108265439658, "alias": "accusantium-inventore_lsat-ar_cot" }, "accusantium-inventore_logiqa_cot": { "acc,none": 0.3274760383386581, "acc_stderr,none": 0.01877170136786437, "alias": "accusantium-inventore_logiqa_cot" }, "accusantium-inventore_logiqa2_cot": { "acc,none": 0.30470737913486007, "acc_stderr,none": 0.01161280687039332, "alias": "accusantium-inventore_logiqa2_cot" } }, "configs": { "accusantium-inventore_logiqa2_cot": { "task": "accusantium-inventore_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "accusantium-inventore-logiqa2/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "accusantium-inventore_logiqa_cot": { "task": "accusantium-inventore_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "accusantium-inventore-logiqa/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "accusantium-inventore_lsat-ar_cot": { "task": "accusantium-inventore_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "accusantium-inventore-lsat-ar/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "accusantium-inventore_lsat-lr_cot": { "task": "accusantium-inventore_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "accusantium-inventore-lsat-lr/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "accusantium-inventore_lsat-rc_cot": { "task": "accusantium-inventore_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "accusantium-inventore-lsat-rc/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "amet-ullam_logiqa2_cot": { "task": "amet-ullam_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "amet-ullam-logiqa2/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "amet-ullam_logiqa_cot": { "task": "amet-ullam_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "amet-ullam-logiqa/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "amet-ullam_lsat-ar_cot": { "task": "amet-ullam_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "amet-ullam-lsat-ar/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "amet-ullam_lsat-lr_cot": { "task": "amet-ullam_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "amet-ullam-lsat-lr/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "amet-ullam_lsat-rc_cot": { "task": "amet-ullam_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "amet-ullam-lsat-rc/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "illum-eaque_logiqa2_cot": { "task": "illum-eaque_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "illum-eaque-logiqa2/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "illum-eaque_logiqa_cot": { "task": "illum-eaque_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "illum-eaque-logiqa/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "illum-eaque_lsat-ar_cot": { "task": "illum-eaque_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "illum-eaque-lsat-ar/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "illum-eaque_lsat-lr_cot": { "task": "illum-eaque_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "illum-eaque-lsat-lr/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "illum-eaque_lsat-rc_cot": { "task": "illum-eaque_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "illum-eaque-lsat-rc/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "libero-exercitationem_logiqa2_cot": { "task": "libero-exercitationem_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "libero-exercitationem-logiqa2/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "libero-exercitationem_logiqa_cot": { "task": "libero-exercitationem_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "libero-exercitationem-logiqa/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "libero-exercitationem_lsat-ar_cot": { "task": "libero-exercitationem_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "libero-exercitationem-lsat-ar/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "libero-exercitationem_lsat-lr_cot": { "task": "libero-exercitationem_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "libero-exercitationem-lsat-lr/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "libero-exercitationem_lsat-rc_cot": { "task": "libero-exercitationem_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "libero-exercitationem-lsat-rc/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "magnam-eius_logiqa2_cot": { "task": "magnam-eius_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "magnam-eius-logiqa2/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "magnam-eius_logiqa_cot": { "task": "magnam-eius_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "magnam-eius-logiqa/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "magnam-eius_lsat-ar_cot": { "task": "magnam-eius_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "magnam-eius-lsat-ar/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "magnam-eius_lsat-lr_cot": { "task": "magnam-eius_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "magnam-eius-lsat-lr/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "magnam-eius_lsat-rc_cot": { "task": "magnam-eius_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "magnam-eius-lsat-rc/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "omnis-voluptatibus_logiqa2_cot": { "task": "omnis-voluptatibus_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "omnis-voluptatibus-logiqa2/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "omnis-voluptatibus_logiqa_cot": { "task": "omnis-voluptatibus_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "omnis-voluptatibus-logiqa/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "omnis-voluptatibus_lsat-ar_cot": { "task": "omnis-voluptatibus_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "omnis-voluptatibus-lsat-ar/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "omnis-voluptatibus_lsat-lr_cot": { "task": "omnis-voluptatibus_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "omnis-voluptatibus-lsat-lr/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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 } }, "omnis-voluptatibus_lsat-rc_cot": { "task": "omnis-voluptatibus_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "omnis-voluptatibus-lsat-rc/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [Reasoning: ]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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": { "accusantium-inventore_logiqa2_cot": 0.0, "accusantium-inventore_logiqa_cot": 0.0, "accusantium-inventore_lsat-ar_cot": 0.0, "accusantium-inventore_lsat-lr_cot": 0.0, "accusantium-inventore_lsat-rc_cot": 0.0, "amet-ullam_logiqa2_cot": 0.0, "amet-ullam_logiqa_cot": 0.0, "amet-ullam_lsat-ar_cot": 0.0, "amet-ullam_lsat-lr_cot": 0.0, "amet-ullam_lsat-rc_cot": 0.0, "illum-eaque_logiqa2_cot": 0.0, "illum-eaque_logiqa_cot": 0.0, "illum-eaque_lsat-ar_cot": 0.0, "illum-eaque_lsat-lr_cot": 0.0, "illum-eaque_lsat-rc_cot": 0.0, "libero-exercitationem_logiqa2_cot": 0.0, "libero-exercitationem_logiqa_cot": 0.0, "libero-exercitationem_lsat-ar_cot": 0.0, "libero-exercitationem_lsat-lr_cot": 0.0, "libero-exercitationem_lsat-rc_cot": 0.0, "magnam-eius_logiqa2_cot": 0.0, "magnam-eius_logiqa_cot": 0.0, "magnam-eius_lsat-ar_cot": 0.0, "magnam-eius_lsat-lr_cot": 0.0, "magnam-eius_lsat-rc_cot": 0.0, "omnis-voluptatibus_logiqa2_cot": 0.0, "omnis-voluptatibus_logiqa_cot": 0.0, "omnis-voluptatibus_lsat-ar_cot": 0.0, "omnis-voluptatibus_lsat-lr_cot": 0.0, "omnis-voluptatibus_lsat-rc_cot": 0.0 }, "n-shot": { "accusantium-inventore_logiqa2_cot": 0, "accusantium-inventore_logiqa_cot": 0, "accusantium-inventore_lsat-ar_cot": 0, "accusantium-inventore_lsat-lr_cot": 0, "accusantium-inventore_lsat-rc_cot": 0, "amet-ullam_logiqa2_cot": 0, "amet-ullam_logiqa_cot": 0, "amet-ullam_lsat-ar_cot": 0, "amet-ullam_lsat-lr_cot": 0, "amet-ullam_lsat-rc_cot": 0, "illum-eaque_logiqa2_cot": 0, "illum-eaque_logiqa_cot": 0, "illum-eaque_lsat-ar_cot": 0, "illum-eaque_lsat-lr_cot": 0, "illum-eaque_lsat-rc_cot": 0, "libero-exercitationem_logiqa2_cot": 0, "libero-exercitationem_logiqa_cot": 0, "libero-exercitationem_lsat-ar_cot": 0, "libero-exercitationem_lsat-lr_cot": 0, "libero-exercitationem_lsat-rc_cot": 0, "magnam-eius_logiqa2_cot": 0, "magnam-eius_logiqa_cot": 0, "magnam-eius_lsat-ar_cot": 0, "magnam-eius_lsat-lr_cot": 0, "magnam-eius_lsat-rc_cot": 0, "omnis-voluptatibus_logiqa2_cot": 0, "omnis-voluptatibus_logiqa_cot": 0, "omnis-voluptatibus_lsat-ar_cot": 0, "omnis-voluptatibus_lsat-lr_cot": 0, "omnis-voluptatibus_lsat-rc_cot": 0 }, "config": { "model": "vllm", "model_args": "pretrained=01-ai/Yi-6B,revision=main,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.9,trust_remote_code=true,max_length=4096", "batch_size": "auto", "batch_sizes": [], "device": null, "use_cache": null, "limit": null, "bootstrap_iters": 100000, "gen_kwargs": null }, "git_hash": "5044cf9" }