| { | |
| "results": { | |
| "unde-laudantium_lsat-rc_base": { | |
| "acc,none": 0.30111524163568776, | |
| "acc_stderr,none": 0.028022169587612226, | |
| "alias": "unde-laudantium_lsat-rc_base" | |
| }, | |
| "unde-laudantium_lsat-lr_base": { | |
| "acc,none": 0.23529411764705882, | |
| "acc_stderr,none": 0.018801558887410304, | |
| "alias": "unde-laudantium_lsat-lr_base" | |
| }, | |
| "unde-laudantium_lsat-ar_base": { | |
| "acc,none": 0.20869565217391303, | |
| "acc_stderr,none": 0.026854108265439675, | |
| "alias": "unde-laudantium_lsat-ar_base" | |
| }, | |
| "unde-laudantium_logiqa_base": { | |
| "acc,none": 0.26996805111821087, | |
| "acc_stderr,none": 0.017757716181700637, | |
| "alias": "unde-laudantium_logiqa_base" | |
| }, | |
| "unde-laudantium_logiqa2_base": { | |
| "acc,none": 0.3256997455470738, | |
| "acc_stderr,none": 0.011823533300939599, | |
| "alias": "unde-laudantium_logiqa2_base" | |
| }, | |
| "temporibus-illo_lsat-rc_base": { | |
| "acc,none": 0.26394052044609667, | |
| "acc_stderr,none": 0.02692415564390256, | |
| "alias": "temporibus-illo_lsat-rc_base" | |
| }, | |
| "temporibus-illo_lsat-lr_base": { | |
| "acc,none": 0.21568627450980393, | |
| "acc_stderr,none": 0.018230445049830818, | |
| "alias": "temporibus-illo_lsat-lr_base" | |
| }, | |
| "temporibus-illo_lsat-ar_base": { | |
| "acc,none": 0.1826086956521739, | |
| "acc_stderr,none": 0.025530421952734174, | |
| "alias": "temporibus-illo_lsat-ar_base" | |
| }, | |
| "temporibus-illo_logiqa_base": { | |
| "acc,none": 0.26996805111821087, | |
| "acc_stderr,none": 0.017757716181700637, | |
| "alias": "temporibus-illo_logiqa_base" | |
| }, | |
| "temporibus-illo_logiqa2_base": { | |
| "acc,none": 0.3187022900763359, | |
| "acc_stderr,none": 0.011756362373408389, | |
| "alias": "temporibus-illo_logiqa2_base" | |
| }, | |
| "quo-non_lsat-rc_base": { | |
| "acc,none": 0.24907063197026022, | |
| "acc_stderr,none": 0.02641760298057974, | |
| "alias": "quo-non_lsat-rc_base" | |
| }, | |
| "quo-non_lsat-lr_base": { | |
| "acc,none": 0.2411764705882353, | |
| "acc_stderr,none": 0.018961774215004727, | |
| "alias": "quo-non_lsat-lr_base" | |
| }, | |
| "quo-non_lsat-ar_base": { | |
| "acc,none": 0.20434782608695654, | |
| "acc_stderr,none": 0.026645808150011344, | |
| "alias": "quo-non_lsat-ar_base" | |
| }, | |
| "quo-non_logiqa_base": { | |
| "acc,none": 0.25878594249201275, | |
| "acc_stderr,none": 0.01751871129783383, | |
| "alias": "quo-non_logiqa_base" | |
| }, | |
| "quo-non_logiqa2_base": { | |
| "acc,none": 0.30279898218829515, | |
| "acc_stderr,none": 0.011592260158888737, | |
| "alias": "quo-non_logiqa2_base" | |
| }, | |
| "magni-excepturi_lsat-rc_base": { | |
| "acc,none": 0.26022304832713755, | |
| "acc_stderr,none": 0.02680130130545777, | |
| "alias": "magni-excepturi_lsat-rc_base" | |
| }, | |
| "magni-excepturi_lsat-lr_base": { | |
| "acc,none": 0.22745098039215686, | |
| "acc_stderr,none": 0.018580099622603333, | |
| "alias": "magni-excepturi_lsat-lr_base" | |
| }, | |
| "magni-excepturi_lsat-ar_base": { | |
| "acc,none": 0.17391304347826086, | |
| "acc_stderr,none": 0.02504731738604972, | |
| "alias": "magni-excepturi_lsat-ar_base" | |
| }, | |
| "magni-excepturi_logiqa_base": { | |
| "acc,none": 0.25878594249201275, | |
| "acc_stderr,none": 0.01751871129783383, | |
| "alias": "magni-excepturi_logiqa_base" | |
| }, | |
| "magni-excepturi_logiqa2_base": { | |
| "acc,none": 0.30725190839694655, | |
| "acc_stderr,none": 0.011639836259579924, | |
| "alias": "magni-excepturi_logiqa2_base" | |
| }, | |
| "laboriosam-numquam_lsat-rc_base": { | |
| "acc,none": 0.27137546468401486, | |
| "acc_stderr,none": 0.027162503089239523, | |
| "alias": "laboriosam-numquam_lsat-rc_base" | |
| }, | |
| "laboriosam-numquam_lsat-lr_base": { | |
| "acc,none": 0.21372549019607842, | |
| "acc_stderr,none": 0.01817006027631824, | |
| "alias": "laboriosam-numquam_lsat-lr_base" | |
| }, | |
| "laboriosam-numquam_lsat-ar_base": { | |
| "acc,none": 0.21304347826086956, | |
| "acc_stderr,none": 0.027057754389936177, | |
| "alias": "laboriosam-numquam_lsat-ar_base" | |
| }, | |
| "laboriosam-numquam_logiqa_base": { | |
| "acc,none": 0.25559105431309903, | |
| "acc_stderr,none": 0.01744771697469749, | |
| "alias": "laboriosam-numquam_logiqa_base" | |
| }, | |
| "laboriosam-numquam_logiqa2_base": { | |
| "acc,none": 0.30725190839694655, | |
| "acc_stderr,none": 0.011639836259579922, | |
| "alias": "laboriosam-numquam_logiqa2_base" | |
| }, | |
| "dolore-possimus_lsat-rc_base": { | |
| "acc,none": 0.2862453531598513, | |
| "acc_stderr,none": 0.027610628966374826, | |
| "alias": "dolore-possimus_lsat-rc_base" | |
| }, | |
| "dolore-possimus_lsat-lr_base": { | |
| "acc,none": 0.2196078431372549, | |
| "acc_stderr,none": 0.01834938361142324, | |
| "alias": "dolore-possimus_lsat-lr_base" | |
| }, | |
| "dolore-possimus_lsat-ar_base": { | |
| "acc,none": 0.2217391304347826, | |
| "acc_stderr,none": 0.027451496604058916, | |
| "alias": "dolore-possimus_lsat-ar_base" | |
| }, | |
| "dolore-possimus_logiqa_base": { | |
| "acc,none": 0.2763578274760383, | |
| "acc_stderr,none": 0.01788783625456192, | |
| "alias": "dolore-possimus_logiqa_base" | |
| }, | |
| "dolore-possimus_logiqa2_base": { | |
| "acc,none": 0.2989821882951654, | |
| "acc_stderr,none": 0.011550454987784068, | |
| "alias": "dolore-possimus_logiqa2_base" | |
| } | |
| }, | |
| "configs": { | |
| "dolore-possimus_logiqa2_base": { | |
| "task": "dolore-possimus_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "dolore-possimus-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 | |
| } | |
| }, | |
| "dolore-possimus_logiqa_base": { | |
| "task": "dolore-possimus_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "dolore-possimus-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 | |
| } | |
| }, | |
| "dolore-possimus_lsat-ar_base": { | |
| "task": "dolore-possimus_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "dolore-possimus-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 | |
| } | |
| }, | |
| "dolore-possimus_lsat-lr_base": { | |
| "task": "dolore-possimus_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "dolore-possimus-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 | |
| } | |
| }, | |
| "dolore-possimus_lsat-rc_base": { | |
| "task": "dolore-possimus_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "dolore-possimus-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 | |
| } | |
| }, | |
| "laboriosam-numquam_logiqa2_base": { | |
| "task": "laboriosam-numquam_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "laboriosam-numquam-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 | |
| } | |
| }, | |
| "laboriosam-numquam_logiqa_base": { | |
| "task": "laboriosam-numquam_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "laboriosam-numquam-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 | |
| } | |
| }, | |
| "laboriosam-numquam_lsat-ar_base": { | |
| "task": "laboriosam-numquam_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "laboriosam-numquam-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 | |
| } | |
| }, | |
| "laboriosam-numquam_lsat-lr_base": { | |
| "task": "laboriosam-numquam_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "laboriosam-numquam-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 | |
| } | |
| }, | |
| "laboriosam-numquam_lsat-rc_base": { | |
| "task": "laboriosam-numquam_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "laboriosam-numquam-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 | |
| } | |
| }, | |
| "magni-excepturi_logiqa2_base": { | |
| "task": "magni-excepturi_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "magni-excepturi-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 | |
| } | |
| }, | |
| "magni-excepturi_logiqa_base": { | |
| "task": "magni-excepturi_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "magni-excepturi-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 | |
| } | |
| }, | |
| "magni-excepturi_lsat-ar_base": { | |
| "task": "magni-excepturi_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "magni-excepturi-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 | |
| } | |
| }, | |
| "magni-excepturi_lsat-lr_base": { | |
| "task": "magni-excepturi_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "magni-excepturi-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 | |
| } | |
| }, | |
| "magni-excepturi_lsat-rc_base": { | |
| "task": "magni-excepturi_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "magni-excepturi-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 | |
| } | |
| }, | |
| "quo-non_logiqa2_base": { | |
| "task": "quo-non_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "quo-non-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 | |
| } | |
| }, | |
| "quo-non_logiqa_base": { | |
| "task": "quo-non_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "quo-non-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 | |
| } | |
| }, | |
| "quo-non_lsat-ar_base": { | |
| "task": "quo-non_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "quo-non-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 | |
| } | |
| }, | |
| "quo-non_lsat-lr_base": { | |
| "task": "quo-non_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "quo-non-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 | |
| } | |
| }, | |
| "quo-non_lsat-rc_base": { | |
| "task": "quo-non_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "quo-non-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 | |
| } | |
| }, | |
| "temporibus-illo_logiqa2_base": { | |
| "task": "temporibus-illo_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "temporibus-illo-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 | |
| } | |
| }, | |
| "temporibus-illo_logiqa_base": { | |
| "task": "temporibus-illo_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "temporibus-illo-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 | |
| } | |
| }, | |
| "temporibus-illo_lsat-ar_base": { | |
| "task": "temporibus-illo_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "temporibus-illo-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 | |
| } | |
| }, | |
| "temporibus-illo_lsat-lr_base": { | |
| "task": "temporibus-illo_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "temporibus-illo-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 | |
| } | |
| }, | |
| "temporibus-illo_lsat-rc_base": { | |
| "task": "temporibus-illo_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "temporibus-illo-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 | |
| } | |
| }, | |
| "unde-laudantium_logiqa2_base": { | |
| "task": "unde-laudantium_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "unde-laudantium-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 | |
| } | |
| }, | |
| "unde-laudantium_logiqa_base": { | |
| "task": "unde-laudantium_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "unde-laudantium-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 | |
| } | |
| }, | |
| "unde-laudantium_lsat-ar_base": { | |
| "task": "unde-laudantium_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "unde-laudantium-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 | |
| } | |
| }, | |
| "unde-laudantium_lsat-lr_base": { | |
| "task": "unde-laudantium_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "unde-laudantium-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 | |
| } | |
| }, | |
| "unde-laudantium_lsat-rc_base": { | |
| "task": "unde-laudantium_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "logikon/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "unde-laudantium-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": { | |
| "dolore-possimus_logiqa2_base": 0.0, | |
| "dolore-possimus_logiqa_base": 0.0, | |
| "dolore-possimus_lsat-ar_base": 0.0, | |
| "dolore-possimus_lsat-lr_base": 0.0, | |
| "dolore-possimus_lsat-rc_base": 0.0, | |
| "laboriosam-numquam_logiqa2_base": 0.0, | |
| "laboriosam-numquam_logiqa_base": 0.0, | |
| "laboriosam-numquam_lsat-ar_base": 0.0, | |
| "laboriosam-numquam_lsat-lr_base": 0.0, | |
| "laboriosam-numquam_lsat-rc_base": 0.0, | |
| "magni-excepturi_logiqa2_base": 0.0, | |
| "magni-excepturi_logiqa_base": 0.0, | |
| "magni-excepturi_lsat-ar_base": 0.0, | |
| "magni-excepturi_lsat-lr_base": 0.0, | |
| "magni-excepturi_lsat-rc_base": 0.0, | |
| "quo-non_logiqa2_base": 0.0, | |
| "quo-non_logiqa_base": 0.0, | |
| "quo-non_lsat-ar_base": 0.0, | |
| "quo-non_lsat-lr_base": 0.0, | |
| "quo-non_lsat-rc_base": 0.0, | |
| "temporibus-illo_logiqa2_base": 0.0, | |
| "temporibus-illo_logiqa_base": 0.0, | |
| "temporibus-illo_lsat-ar_base": 0.0, | |
| "temporibus-illo_lsat-lr_base": 0.0, | |
| "temporibus-illo_lsat-rc_base": 0.0, | |
| "unde-laudantium_logiqa2_base": 0.0, | |
| "unde-laudantium_logiqa_base": 0.0, | |
| "unde-laudantium_lsat-ar_base": 0.0, | |
| "unde-laudantium_lsat-lr_base": 0.0, | |
| "unde-laudantium_lsat-rc_base": 0.0 | |
| }, | |
| "n-shot": { | |
| "dolore-possimus_logiqa2_base": 0, | |
| "dolore-possimus_logiqa_base": 0, | |
| "dolore-possimus_lsat-ar_base": 0, | |
| "dolore-possimus_lsat-lr_base": 0, | |
| "dolore-possimus_lsat-rc_base": 0, | |
| "laboriosam-numquam_logiqa2_base": 0, | |
| "laboriosam-numquam_logiqa_base": 0, | |
| "laboriosam-numquam_lsat-ar_base": 0, | |
| "laboriosam-numquam_lsat-lr_base": 0, | |
| "laboriosam-numquam_lsat-rc_base": 0, | |
| "magni-excepturi_logiqa2_base": 0, | |
| "magni-excepturi_logiqa_base": 0, | |
| "magni-excepturi_lsat-ar_base": 0, | |
| "magni-excepturi_lsat-lr_base": 0, | |
| "magni-excepturi_lsat-rc_base": 0, | |
| "quo-non_logiqa2_base": 0, | |
| "quo-non_logiqa_base": 0, | |
| "quo-non_lsat-ar_base": 0, | |
| "quo-non_lsat-lr_base": 0, | |
| "quo-non_lsat-rc_base": 0, | |
| "temporibus-illo_logiqa2_base": 0, | |
| "temporibus-illo_logiqa_base": 0, | |
| "temporibus-illo_lsat-ar_base": 0, | |
| "temporibus-illo_lsat-lr_base": 0, | |
| "temporibus-illo_lsat-rc_base": 0, | |
| "unde-laudantium_logiqa2_base": 0, | |
| "unde-laudantium_logiqa_base": 0, | |
| "unde-laudantium_lsat-ar_base": 0, | |
| "unde-laudantium_lsat-lr_base": 0, | |
| "unde-laudantium_lsat-rc_base": 0 | |
| }, | |
| "config": { | |
| "model": "vllm", | |
| "model_args": "pretrained=mistralai/Mistral-7B-v0.1,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" | |
| } |