{
"results": {
"molestiae-aperiam_lsat-rc_cot": {
"acc,none": 0.4758364312267658,
"acc_stderr,none": 0.030506674211283072,
"alias": "molestiae-aperiam_lsat-rc_cot"
},
"molestiae-aperiam_lsat-lr_cot": {
"acc,none": 0.4196078431372549,
"acc_stderr,none": 0.021873771696750574,
"alias": "molestiae-aperiam_lsat-lr_cot"
},
"molestiae-aperiam_lsat-ar_cot": {
"acc,none": 0.24347826086956523,
"acc_stderr,none": 0.02836109930007507,
"alias": "molestiae-aperiam_lsat-ar_cot"
},
"molestiae-aperiam_logiqa_cot": {
"acc,none": 0.3753993610223642,
"acc_stderr,none": 0.019369034287392513,
"alias": "molestiae-aperiam_logiqa_cot"
},
"molestiae-aperiam_logiqa2_cot": {
"acc,none": 0.477735368956743,
"acc_stderr,none": 0.012602331890057498,
"alias": "molestiae-aperiam_logiqa2_cot"
},
"iure-at_lsat-rc_cot": {
"acc,none": 0.5241635687732342,
"acc_stderr,none": 0.03050667421128308,
"alias": "iure-at_lsat-rc_cot"
},
"iure-at_lsat-lr_cot": {
"acc,none": 0.4666666666666667,
"acc_stderr,none": 0.02211280638156422,
"alias": "iure-at_lsat-lr_cot"
},
"iure-at_lsat-ar_cot": {
"acc,none": 0.19130434782608696,
"acc_stderr,none": 0.025991852462828483,
"alias": "iure-at_lsat-ar_cot"
},
"iure-at_logiqa_cot": {
"acc,none": 0.36421725239616615,
"acc_stderr,none": 0.019248399225001777,
"alias": "iure-at_logiqa_cot"
},
"iure-at_logiqa2_cot": {
"acc,none": 0.4446564885496183,
"acc_stderr,none": 0.012537330526916487,
"alias": "iure-at_logiqa2_cot"
},
"facere-optio_lsat-rc_cot": {
"acc,none": 0.5018587360594795,
"acc_stderr,none": 0.030542150046756426,
"alias": "facere-optio_lsat-rc_cot"
},
"facere-optio_lsat-lr_cot": {
"acc,none": 0.43333333333333335,
"acc_stderr,none": 0.021964230412067975,
"alias": "facere-optio_lsat-lr_cot"
},
"facere-optio_lsat-ar_cot": {
"acc,none": 0.1826086956521739,
"acc_stderr,none": 0.02553042195273417,
"alias": "facere-optio_lsat-ar_cot"
},
"facere-optio_logiqa_cot": {
"acc,none": 0.3706070287539936,
"acc_stderr,none": 0.019318693909977667,
"alias": "facere-optio_logiqa_cot"
},
"facere-optio_logiqa2_cot": {
"acc,none": 0.44656488549618323,
"acc_stderr,none": 0.012542599303456,
"alias": "facere-optio_logiqa2_cot"
},
"et-praesentium_lsat-rc_cot": {
"acc,none": 0.49814126394052044,
"acc_stderr,none": 0.030542150046756422,
"alias": "et-praesentium_lsat-rc_cot"
},
"et-praesentium_lsat-lr_cot": {
"acc,none": 0.3784313725490196,
"acc_stderr,none": 0.02149706741180824,
"alias": "et-praesentium_lsat-lr_cot"
},
"et-praesentium_lsat-ar_cot": {
"acc,none": 0.2217391304347826,
"acc_stderr,none": 0.02745149660405892,
"alias": "et-praesentium_lsat-ar_cot"
},
"et-praesentium_logiqa_cot": {
"acc,none": 0.36261980830670926,
"acc_stderr,none": 0.019230254618400246,
"alias": "et-praesentium_logiqa_cot"
},
"et-praesentium_logiqa2_cot": {
"acc,none": 0.46946564885496184,
"acc_stderr,none": 0.012591300013425958,
"alias": "et-praesentium_logiqa2_cot"
},
"eligendi-commodi_lsat-rc_cot": {
"acc,none": 0.5018587360594795,
"acc_stderr,none": 0.030542150046756426,
"alias": "eligendi-commodi_lsat-rc_cot"
},
"eligendi-commodi_lsat-lr_cot": {
"acc,none": 0.42549019607843136,
"acc_stderr,none": 0.021914653579107275,
"alias": "eligendi-commodi_lsat-lr_cot"
},
"eligendi-commodi_lsat-ar_cot": {
"acc,none": 0.23478260869565218,
"acc_stderr,none": 0.02800964707093011,
"alias": "eligendi-commodi_lsat-ar_cot"
},
"eligendi-commodi_logiqa_cot": {
"acc,none": 0.3466453674121406,
"acc_stderr,none": 0.019036064999420094,
"alias": "eligendi-commodi_logiqa_cot"
},
"eligendi-commodi_logiqa2_cot": {
"acc,none": 0.4643765903307888,
"acc_stderr,none": 0.012582786901750204,
"alias": "eligendi-commodi_logiqa2_cot"
},
"doloremque-rem_lsat-rc_cot": {
"acc,none": 0.5092936802973977,
"acc_stderr,none": 0.030537084593525398,
"alias": "doloremque-rem_lsat-rc_cot"
},
"doloremque-rem_lsat-lr_cot": {
"acc,none": 0.4117647058823529,
"acc_stderr,none": 0.02181429628344194,
"alias": "doloremque-rem_lsat-lr_cot"
},
"doloremque-rem_lsat-ar_cot": {
"acc,none": 0.21304347826086956,
"acc_stderr,none": 0.027057754389936194,
"alias": "doloremque-rem_lsat-ar_cot"
},
"doloremque-rem_logiqa_cot": {
"acc,none": 0.3610223642172524,
"acc_stderr,none": 0.019211880355748154,
"alias": "doloremque-rem_logiqa_cot"
},
"doloremque-rem_logiqa2_cot": {
"acc,none": 0.46119592875318066,
"acc_stderr,none": 0.012576797669813296,
"alias": "doloremque-rem_logiqa2_cot"
}
},
"configs": {
"doloremque-rem_logiqa2_cot": {
"task": "doloremque-rem_logiqa2_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "doloremque-rem-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"doloremque-rem_logiqa_cot": {
"task": "doloremque-rem_logiqa_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "doloremque-rem-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"doloremque-rem_lsat-ar_cot": {
"task": "doloremque-rem_lsat-ar_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "doloremque-rem-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"doloremque-rem_lsat-lr_cot": {
"task": "doloremque-rem_lsat-lr_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "doloremque-rem-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"doloremque-rem_lsat-rc_cot": {
"task": "doloremque-rem_lsat-rc_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "doloremque-rem-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"eligendi-commodi_logiqa2_cot": {
"task": "eligendi-commodi_logiqa2_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "eligendi-commodi-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"eligendi-commodi_logiqa_cot": {
"task": "eligendi-commodi_logiqa_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "eligendi-commodi-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"eligendi-commodi_lsat-ar_cot": {
"task": "eligendi-commodi_lsat-ar_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "eligendi-commodi-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"eligendi-commodi_lsat-lr_cot": {
"task": "eligendi-commodi_lsat-lr_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "eligendi-commodi-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"eligendi-commodi_lsat-rc_cot": {
"task": "eligendi-commodi_lsat-rc_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "eligendi-commodi-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"et-praesentium_logiqa2_cot": {
"task": "et-praesentium_logiqa2_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "et-praesentium-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"et-praesentium_logiqa_cot": {
"task": "et-praesentium_logiqa_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "et-praesentium-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"et-praesentium_lsat-ar_cot": {
"task": "et-praesentium_lsat-ar_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "et-praesentium-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"et-praesentium_lsat-lr_cot": {
"task": "et-praesentium_lsat-lr_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "et-praesentium-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"et-praesentium_lsat-rc_cot": {
"task": "et-praesentium_lsat-rc_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "et-praesentium-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"facere-optio_logiqa2_cot": {
"task": "facere-optio_logiqa2_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "facere-optio-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"facere-optio_logiqa_cot": {
"task": "facere-optio_logiqa_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "facere-optio-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"facere-optio_lsat-ar_cot": {
"task": "facere-optio_lsat-ar_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "facere-optio-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"facere-optio_lsat-lr_cot": {
"task": "facere-optio_lsat-lr_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "facere-optio-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"facere-optio_lsat-rc_cot": {
"task": "facere-optio_lsat-rc_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "facere-optio-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"iure-at_logiqa2_cot": {
"task": "iure-at_logiqa2_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "iure-at-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"iure-at_logiqa_cot": {
"task": "iure-at_logiqa_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "iure-at-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"iure-at_lsat-ar_cot": {
"task": "iure-at_lsat-ar_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "iure-at-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"iure-at_lsat-lr_cot": {
"task": "iure-at_lsat-lr_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "iure-at-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"iure-at_lsat-rc_cot": {
"task": "iure-at_lsat-rc_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "iure-at-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"molestiae-aperiam_logiqa2_cot": {
"task": "molestiae-aperiam_logiqa2_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "molestiae-aperiam-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"molestiae-aperiam_logiqa_cot": {
"task": "molestiae-aperiam_logiqa_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "molestiae-aperiam-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"molestiae-aperiam_lsat-ar_cot": {
"task": "molestiae-aperiam_lsat-ar_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "molestiae-aperiam-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"molestiae-aperiam_lsat-lr_cot": {
"task": "molestiae-aperiam_lsat-lr_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "molestiae-aperiam-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"molestiae-aperiam_lsat-rc_cot": {
"task": "molestiae-aperiam_lsat-rc_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "molestiae-aperiam-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
}
},
"versions": {
"doloremque-rem_logiqa2_cot": 0.0,
"doloremque-rem_logiqa_cot": 0.0,
"doloremque-rem_lsat-ar_cot": 0.0,
"doloremque-rem_lsat-lr_cot": 0.0,
"doloremque-rem_lsat-rc_cot": 0.0,
"eligendi-commodi_logiqa2_cot": 0.0,
"eligendi-commodi_logiqa_cot": 0.0,
"eligendi-commodi_lsat-ar_cot": 0.0,
"eligendi-commodi_lsat-lr_cot": 0.0,
"eligendi-commodi_lsat-rc_cot": 0.0,
"et-praesentium_logiqa2_cot": 0.0,
"et-praesentium_logiqa_cot": 0.0,
"et-praesentium_lsat-ar_cot": 0.0,
"et-praesentium_lsat-lr_cot": 0.0,
"et-praesentium_lsat-rc_cot": 0.0,
"facere-optio_logiqa2_cot": 0.0,
"facere-optio_logiqa_cot": 0.0,
"facere-optio_lsat-ar_cot": 0.0,
"facere-optio_lsat-lr_cot": 0.0,
"facere-optio_lsat-rc_cot": 0.0,
"iure-at_logiqa2_cot": 0.0,
"iure-at_logiqa_cot": 0.0,
"iure-at_lsat-ar_cot": 0.0,
"iure-at_lsat-lr_cot": 0.0,
"iure-at_lsat-rc_cot": 0.0,
"molestiae-aperiam_logiqa2_cot": 0.0,
"molestiae-aperiam_logiqa_cot": 0.0,
"molestiae-aperiam_lsat-ar_cot": 0.0,
"molestiae-aperiam_lsat-lr_cot": 0.0,
"molestiae-aperiam_lsat-rc_cot": 0.0
},
"n-shot": {
"doloremque-rem_logiqa2_cot": 0,
"doloremque-rem_logiqa_cot": 0,
"doloremque-rem_lsat-ar_cot": 0,
"doloremque-rem_lsat-lr_cot": 0,
"doloremque-rem_lsat-rc_cot": 0,
"eligendi-commodi_logiqa2_cot": 0,
"eligendi-commodi_logiqa_cot": 0,
"eligendi-commodi_lsat-ar_cot": 0,
"eligendi-commodi_lsat-lr_cot": 0,
"eligendi-commodi_lsat-rc_cot": 0,
"et-praesentium_logiqa2_cot": 0,
"et-praesentium_logiqa_cot": 0,
"et-praesentium_lsat-ar_cot": 0,
"et-praesentium_lsat-lr_cot": 0,
"et-praesentium_lsat-rc_cot": 0,
"facere-optio_logiqa2_cot": 0,
"facere-optio_logiqa_cot": 0,
"facere-optio_lsat-ar_cot": 0,
"facere-optio_lsat-lr_cot": 0,
"facere-optio_lsat-rc_cot": 0,
"iure-at_logiqa2_cot": 0,
"iure-at_logiqa_cot": 0,
"iure-at_lsat-ar_cot": 0,
"iure-at_lsat-lr_cot": 0,
"iure-at_lsat-rc_cot": 0,
"molestiae-aperiam_logiqa2_cot": 0,
"molestiae-aperiam_logiqa_cot": 0,
"molestiae-aperiam_lsat-ar_cot": 0,
"molestiae-aperiam_lsat-lr_cot": 0,
"molestiae-aperiam_lsat-rc_cot": 0
},
"config": {
"model": "vllm",
"model_args": "pretrained=openchat/openchat-3.5-0106,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": "a1d6b70"
}