| { | |
| "results": { | |
| "omnis-voluptatibus_lsat-rc_base": { | |
| "acc,none": 0.26022304832713755, | |
| "acc_stderr,none": 0.02680130130545777, | |
| "alias": "omnis-voluptatibus_lsat-rc_base" | |
| }, | |
| "omnis-voluptatibus_lsat-lr_base": { | |
| "acc,none": 0.22549019607843138, | |
| "acc_stderr,none": 0.018523301713974177, | |
| "alias": "omnis-voluptatibus_lsat-lr_base" | |
| }, | |
| "omnis-voluptatibus_lsat-ar_base": { | |
| "acc,none": 0.2, | |
| "acc_stderr,none": 0.026432744018203558, | |
| "alias": "omnis-voluptatibus_lsat-ar_base" | |
| }, | |
| "omnis-voluptatibus_logiqa_base": { | |
| "acc,none": 0.2731629392971246, | |
| "acc_stderr,none": 0.017823353125231246, | |
| "alias": "omnis-voluptatibus_logiqa_base" | |
| }, | |
| "omnis-voluptatibus_logiqa2_base": { | |
| "acc,none": 0.29961832061068705, | |
| "acc_stderr,none": 0.011557488735539868, | |
| "alias": "omnis-voluptatibus_logiqa2_base" | |
| }, | |
| "magnam-eius_lsat-rc_base": { | |
| "acc,none": 0.26765799256505574, | |
| "acc_stderr,none": 0.027044545314587297, | |
| "alias": "magnam-eius_lsat-rc_base" | |
| }, | |
| "magnam-eius_lsat-lr_base": { | |
| "acc,none": 0.23333333333333334, | |
| "acc_stderr,none": 0.018747043716590736, | |
| "alias": "magnam-eius_lsat-lr_base" | |
| }, | |
| "magnam-eius_lsat-ar_base": { | |
| "acc,none": 0.1956521739130435, | |
| "acc_stderr,none": 0.026214799709819582, | |
| "alias": "magnam-eius_lsat-ar_base" | |
| }, | |
| "magnam-eius_logiqa_base": { | |
| "acc,none": 0.26517571884984026, | |
| "acc_stderr,none": 0.017657069155771605, | |
| "alias": "magnam-eius_logiqa_base" | |
| }, | |
| "magnam-eius_logiqa2_base": { | |
| "acc,none": 0.30025445292620867, | |
| "acc_stderr,none": 0.011564495931583802, | |
| "alias": "magnam-eius_logiqa2_base" | |
| }, | |
| "libero-exercitationem_lsat-rc_base": { | |
| "acc,none": 0.2899628252788104, | |
| "acc_stderr,none": 0.0277168778552269, | |
| "alias": "libero-exercitationem_lsat-rc_base" | |
| }, | |
| "libero-exercitationem_lsat-lr_base": { | |
| "acc,none": 0.23333333333333334, | |
| "acc_stderr,none": 0.01874704371659074, | |
| "alias": "libero-exercitationem_lsat-lr_base" | |
| }, | |
| "libero-exercitationem_lsat-ar_base": { | |
| "acc,none": 0.2565217391304348, | |
| "acc_stderr,none": 0.02885881431530565, | |
| "alias": "libero-exercitationem_lsat-ar_base" | |
| }, | |
| "libero-exercitationem_logiqa_base": { | |
| "acc,none": 0.3003194888178914, | |
| "acc_stderr,none": 0.01833587493212361, | |
| "alias": "libero-exercitationem_logiqa_base" | |
| }, | |
| "libero-exercitationem_logiqa2_base": { | |
| "acc,none": 0.2913486005089059, | |
| "acc_stderr,none": 0.011463961006875415, | |
| "alias": "libero-exercitationem_logiqa2_base" | |
| }, | |
| "illum-eaque_lsat-rc_base": { | |
| "acc,none": 0.30855018587360594, | |
| "acc_stderr,none": 0.02821472627233907, | |
| "alias": "illum-eaque_lsat-rc_base" | |
| }, | |
| "illum-eaque_lsat-lr_base": { | |
| "acc,none": 0.23529411764705882, | |
| "acc_stderr,none": 0.018801558887410308, | |
| "alias": "illum-eaque_lsat-lr_base" | |
| }, | |
| "illum-eaque_lsat-ar_base": { | |
| "acc,none": 0.21304347826086956, | |
| "acc_stderr,none": 0.027057754389936205, | |
| "alias": "illum-eaque_lsat-ar_base" | |
| }, | |
| "illum-eaque_logiqa_base": { | |
| "acc,none": 0.2715654952076677, | |
| "acc_stderr,none": 0.017790679673144884, | |
| "alias": "illum-eaque_logiqa_base" | |
| }, | |
| "illum-eaque_logiqa2_base": { | |
| "acc,none": 0.28944020356234096, | |
| "acc_stderr,none": 0.011441728828144178, | |
| "alias": "illum-eaque_logiqa2_base" | |
| }, | |
| "amet-ullam_lsat-rc_base": { | |
| "acc,none": 0.2936802973977695, | |
| "acc_stderr,none": 0.027820867578650918, | |
| "alias": "amet-ullam_lsat-rc_base" | |
| }, | |
| "amet-ullam_lsat-lr_base": { | |
| "acc,none": 0.2235294117647059, | |
| "acc_stderr,none": 0.018465920069400513, | |
| "alias": "amet-ullam_lsat-lr_base" | |
| }, | |
| "amet-ullam_lsat-ar_base": { | |
| "acc,none": 0.21739130434782608, | |
| "acc_stderr,none": 0.027256850838819964, | |
| "alias": "amet-ullam_lsat-ar_base" | |
| }, | |
| "amet-ullam_logiqa_base": { | |
| "acc,none": 0.28434504792332266, | |
| "acc_stderr,none": 0.018044076774157373, | |
| "alias": "amet-ullam_logiqa_base" | |
| }, | |
| "amet-ullam_logiqa2_base": { | |
| "acc,none": 0.2881679389312977, | |
| "acc_stderr,none": 0.011426770634965262, | |
| "alias": "amet-ullam_logiqa2_base" | |
| }, | |
| "accusantium-inventore_lsat-rc_base": { | |
| "acc,none": 0.2936802973977695, | |
| "acc_stderr,none": 0.02782086757865092, | |
| "alias": "accusantium-inventore_lsat-rc_base" | |
| }, | |
| "accusantium-inventore_lsat-lr_base": { | |
| "acc,none": 0.20980392156862746, | |
| "acc_stderr,none": 0.01804742911247611, | |
| "alias": "accusantium-inventore_lsat-lr_base" | |
| }, | |
| "accusantium-inventore_lsat-ar_base": { | |
| "acc,none": 0.23043478260869565, | |
| "acc_stderr,none": 0.027827807522276156, | |
| "alias": "accusantium-inventore_lsat-ar_base" | |
| }, | |
| "accusantium-inventore_logiqa_base": { | |
| "acc,none": 0.3019169329073482, | |
| "acc_stderr,none": 0.018363576929614513, | |
| "alias": "accusantium-inventore_logiqa_base" | |
| }, | |
| "accusantium-inventore_logiqa2_base": { | |
| "acc,none": 0.2913486005089059, | |
| "acc_stderr,none": 0.011463961006875417, | |
| "alias": "accusantium-inventore_logiqa2_base" | |
| } | |
| }, | |
| "configs": { | |
| "accusantium-inventore_logiqa2_base": { | |
| "task": "accusantium-inventore_logiqa2_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "accusantium-inventore_logiqa_base": { | |
| "task": "accusantium-inventore_logiqa_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "accusantium-inventore_lsat-ar_base": { | |
| "task": "accusantium-inventore_lsat-ar_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "accusantium-inventore_lsat-lr_base": { | |
| "task": "accusantium-inventore_lsat-lr_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "accusantium-inventore_lsat-rc_base": { | |
| "task": "accusantium-inventore_lsat-rc_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "amet-ullam_logiqa2_base": { | |
| "task": "amet-ullam_logiqa2_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "amet-ullam_logiqa_base": { | |
| "task": "amet-ullam_logiqa_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "amet-ullam_lsat-ar_base": { | |
| "task": "amet-ullam_lsat-ar_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "amet-ullam_lsat-lr_base": { | |
| "task": "amet-ullam_lsat-lr_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "amet-ullam_lsat-rc_base": { | |
| "task": "amet-ullam_lsat-rc_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "illum-eaque_logiqa2_base": { | |
| "task": "illum-eaque_logiqa2_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "illum-eaque_logiqa_base": { | |
| "task": "illum-eaque_logiqa_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "illum-eaque_lsat-ar_base": { | |
| "task": "illum-eaque_lsat-ar_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "illum-eaque_lsat-lr_base": { | |
| "task": "illum-eaque_lsat-lr_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "illum-eaque_lsat-rc_base": { | |
| "task": "illum-eaque_lsat-rc_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "libero-exercitationem_logiqa2_base": { | |
| "task": "libero-exercitationem_logiqa2_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "libero-exercitationem_logiqa_base": { | |
| "task": "libero-exercitationem_logiqa_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "libero-exercitationem_lsat-ar_base": { | |
| "task": "libero-exercitationem_lsat-ar_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "libero-exercitationem_lsat-lr_base": { | |
| "task": "libero-exercitationem_lsat-lr_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "libero-exercitationem_lsat-rc_base": { | |
| "task": "libero-exercitationem_lsat-rc_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "magnam-eius_logiqa2_base": { | |
| "task": "magnam-eius_logiqa2_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "magnam-eius_logiqa_base": { | |
| "task": "magnam-eius_logiqa_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "magnam-eius_lsat-ar_base": { | |
| "task": "magnam-eius_lsat-ar_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "magnam-eius_lsat-lr_base": { | |
| "task": "magnam-eius_lsat-lr_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "magnam-eius_lsat-rc_base": { | |
| "task": "magnam-eius_lsat-rc_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "omnis-voluptatibus_logiqa2_base": { | |
| "task": "omnis-voluptatibus_logiqa2_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "omnis-voluptatibus_logiqa_base": { | |
| "task": "omnis-voluptatibus_logiqa_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "omnis-voluptatibus_lsat-ar_base": { | |
| "task": "omnis-voluptatibus_lsat-ar_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "omnis-voluptatibus_lsat-lr_base": { | |
| "task": "omnis-voluptatibus_lsat-lr_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "omnis-voluptatibus_lsat-rc_base": { | |
| "task": "omnis-voluptatibus_lsat-rc_base", | |
| "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(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
| "accusantium-inventore_logiqa2_base": 0.0, | |
| "accusantium-inventore_logiqa_base": 0.0, | |
| "accusantium-inventore_lsat-ar_base": 0.0, | |
| "accusantium-inventore_lsat-lr_base": 0.0, | |
| "accusantium-inventore_lsat-rc_base": 0.0, | |
| "amet-ullam_logiqa2_base": 0.0, | |
| "amet-ullam_logiqa_base": 0.0, | |
| "amet-ullam_lsat-ar_base": 0.0, | |
| "amet-ullam_lsat-lr_base": 0.0, | |
| "amet-ullam_lsat-rc_base": 0.0, | |
| "illum-eaque_logiqa2_base": 0.0, | |
| "illum-eaque_logiqa_base": 0.0, | |
| "illum-eaque_lsat-ar_base": 0.0, | |
| "illum-eaque_lsat-lr_base": 0.0, | |
| "illum-eaque_lsat-rc_base": 0.0, | |
| "libero-exercitationem_logiqa2_base": 0.0, | |
| "libero-exercitationem_logiqa_base": 0.0, | |
| "libero-exercitationem_lsat-ar_base": 0.0, | |
| "libero-exercitationem_lsat-lr_base": 0.0, | |
| "libero-exercitationem_lsat-rc_base": 0.0, | |
| "magnam-eius_logiqa2_base": 0.0, | |
| "magnam-eius_logiqa_base": 0.0, | |
| "magnam-eius_lsat-ar_base": 0.0, | |
| "magnam-eius_lsat-lr_base": 0.0, | |
| "magnam-eius_lsat-rc_base": 0.0, | |
| "omnis-voluptatibus_logiqa2_base": 0.0, | |
| "omnis-voluptatibus_logiqa_base": 0.0, | |
| "omnis-voluptatibus_lsat-ar_base": 0.0, | |
| "omnis-voluptatibus_lsat-lr_base": 0.0, | |
| "omnis-voluptatibus_lsat-rc_base": 0.0 | |
| }, | |
| "n-shot": { | |
| "accusantium-inventore_logiqa2_base": 0, | |
| "accusantium-inventore_logiqa_base": 0, | |
| "accusantium-inventore_lsat-ar_base": 0, | |
| "accusantium-inventore_lsat-lr_base": 0, | |
| "accusantium-inventore_lsat-rc_base": 0, | |
| "amet-ullam_logiqa2_base": 0, | |
| "amet-ullam_logiqa_base": 0, | |
| "amet-ullam_lsat-ar_base": 0, | |
| "amet-ullam_lsat-lr_base": 0, | |
| "amet-ullam_lsat-rc_base": 0, | |
| "illum-eaque_logiqa2_base": 0, | |
| "illum-eaque_logiqa_base": 0, | |
| "illum-eaque_lsat-ar_base": 0, | |
| "illum-eaque_lsat-lr_base": 0, | |
| "illum-eaque_lsat-rc_base": 0, | |
| "libero-exercitationem_logiqa2_base": 0, | |
| "libero-exercitationem_logiqa_base": 0, | |
| "libero-exercitationem_lsat-ar_base": 0, | |
| "libero-exercitationem_lsat-lr_base": 0, | |
| "libero-exercitationem_lsat-rc_base": 0, | |
| "magnam-eius_logiqa2_base": 0, | |
| "magnam-eius_logiqa_base": 0, | |
| "magnam-eius_lsat-ar_base": 0, | |
| "magnam-eius_lsat-lr_base": 0, | |
| "magnam-eius_lsat-rc_base": 0, | |
| "omnis-voluptatibus_logiqa2_base": 0, | |
| "omnis-voluptatibus_logiqa_base": 0, | |
| "omnis-voluptatibus_lsat-ar_base": 0, | |
| "omnis-voluptatibus_lsat-lr_base": 0, | |
| "omnis-voluptatibus_lsat-rc_base": 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" | |
| } |