cot-eval-results / data /01-ai /Yi-6B /base /24-02-03-02:34:08.json
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{
"results": {
"omnis-voluptatibus_lsat-rc_base": {
"acc,none": 0.26022304832713755,
"acc_stderr,none": 0.02680130130545777,
"alias": "omnis-voluptatibus_lsat-rc_base"
},
"omnis-voluptatibus_lsat-lr_base": {
"acc,none": 0.22549019607843138,
"acc_stderr,none": 0.018523301713974177,
"alias": "omnis-voluptatibus_lsat-lr_base"
},
"omnis-voluptatibus_lsat-ar_base": {
"acc,none": 0.2,
"acc_stderr,none": 0.026432744018203558,
"alias": "omnis-voluptatibus_lsat-ar_base"
},
"omnis-voluptatibus_logiqa_base": {
"acc,none": 0.2731629392971246,
"acc_stderr,none": 0.017823353125231246,
"alias": "omnis-voluptatibus_logiqa_base"
},
"omnis-voluptatibus_logiqa2_base": {
"acc,none": 0.29961832061068705,
"acc_stderr,none": 0.011557488735539868,
"alias": "omnis-voluptatibus_logiqa2_base"
},
"magnam-eius_lsat-rc_base": {
"acc,none": 0.26765799256505574,
"acc_stderr,none": 0.027044545314587297,
"alias": "magnam-eius_lsat-rc_base"
},
"magnam-eius_lsat-lr_base": {
"acc,none": 0.23333333333333334,
"acc_stderr,none": 0.018747043716590736,
"alias": "magnam-eius_lsat-lr_base"
},
"magnam-eius_lsat-ar_base": {
"acc,none": 0.1956521739130435,
"acc_stderr,none": 0.026214799709819582,
"alias": "magnam-eius_lsat-ar_base"
},
"magnam-eius_logiqa_base": {
"acc,none": 0.26517571884984026,
"acc_stderr,none": 0.017657069155771605,
"alias": "magnam-eius_logiqa_base"
},
"magnam-eius_logiqa2_base": {
"acc,none": 0.30025445292620867,
"acc_stderr,none": 0.011564495931583802,
"alias": "magnam-eius_logiqa2_base"
},
"libero-exercitationem_lsat-rc_base": {
"acc,none": 0.2899628252788104,
"acc_stderr,none": 0.0277168778552269,
"alias": "libero-exercitationem_lsat-rc_base"
},
"libero-exercitationem_lsat-lr_base": {
"acc,none": 0.23333333333333334,
"acc_stderr,none": 0.01874704371659074,
"alias": "libero-exercitationem_lsat-lr_base"
},
"libero-exercitationem_lsat-ar_base": {
"acc,none": 0.2565217391304348,
"acc_stderr,none": 0.02885881431530565,
"alias": "libero-exercitationem_lsat-ar_base"
},
"libero-exercitationem_logiqa_base": {
"acc,none": 0.3003194888178914,
"acc_stderr,none": 0.01833587493212361,
"alias": "libero-exercitationem_logiqa_base"
},
"libero-exercitationem_logiqa2_base": {
"acc,none": 0.2913486005089059,
"acc_stderr,none": 0.011463961006875415,
"alias": "libero-exercitationem_logiqa2_base"
},
"illum-eaque_lsat-rc_base": {
"acc,none": 0.30855018587360594,
"acc_stderr,none": 0.02821472627233907,
"alias": "illum-eaque_lsat-rc_base"
},
"illum-eaque_lsat-lr_base": {
"acc,none": 0.23529411764705882,
"acc_stderr,none": 0.018801558887410308,
"alias": "illum-eaque_lsat-lr_base"
},
"illum-eaque_lsat-ar_base": {
"acc,none": 0.21304347826086956,
"acc_stderr,none": 0.027057754389936205,
"alias": "illum-eaque_lsat-ar_base"
},
"illum-eaque_logiqa_base": {
"acc,none": 0.2715654952076677,
"acc_stderr,none": 0.017790679673144884,
"alias": "illum-eaque_logiqa_base"
},
"illum-eaque_logiqa2_base": {
"acc,none": 0.28944020356234096,
"acc_stderr,none": 0.011441728828144178,
"alias": "illum-eaque_logiqa2_base"
},
"amet-ullam_lsat-rc_base": {
"acc,none": 0.2936802973977695,
"acc_stderr,none": 0.027820867578650918,
"alias": "amet-ullam_lsat-rc_base"
},
"amet-ullam_lsat-lr_base": {
"acc,none": 0.2235294117647059,
"acc_stderr,none": 0.018465920069400513,
"alias": "amet-ullam_lsat-lr_base"
},
"amet-ullam_lsat-ar_base": {
"acc,none": 0.21739130434782608,
"acc_stderr,none": 0.027256850838819964,
"alias": "amet-ullam_lsat-ar_base"
},
"amet-ullam_logiqa_base": {
"acc,none": 0.28434504792332266,
"acc_stderr,none": 0.018044076774157373,
"alias": "amet-ullam_logiqa_base"
},
"amet-ullam_logiqa2_base": {
"acc,none": 0.2881679389312977,
"acc_stderr,none": 0.011426770634965262,
"alias": "amet-ullam_logiqa2_base"
},
"accusantium-inventore_lsat-rc_base": {
"acc,none": 0.2936802973977695,
"acc_stderr,none": 0.02782086757865092,
"alias": "accusantium-inventore_lsat-rc_base"
},
"accusantium-inventore_lsat-lr_base": {
"acc,none": 0.20980392156862746,
"acc_stderr,none": 0.01804742911247611,
"alias": "accusantium-inventore_lsat-lr_base"
},
"accusantium-inventore_lsat-ar_base": {
"acc,none": 0.23043478260869565,
"acc_stderr,none": 0.027827807522276156,
"alias": "accusantium-inventore_lsat-ar_base"
},
"accusantium-inventore_logiqa_base": {
"acc,none": 0.3019169329073482,
"acc_stderr,none": 0.018363576929614513,
"alias": "accusantium-inventore_logiqa_base"
},
"accusantium-inventore_logiqa2_base": {
"acc,none": 0.2913486005089059,
"acc_stderr,none": 0.011463961006875417,
"alias": "accusantium-inventore_logiqa2_base"
}
},
"configs": {
"accusantium-inventore_logiqa2_base": {
"task": "accusantium-inventore_logiqa2_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "accusantium-inventore-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"accusantium-inventore_logiqa_base": {
"task": "accusantium-inventore_logiqa_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "accusantium-inventore-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"accusantium-inventore_lsat-ar_base": {
"task": "accusantium-inventore_lsat-ar_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "accusantium-inventore-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"accusantium-inventore_lsat-lr_base": {
"task": "accusantium-inventore_lsat-lr_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "accusantium-inventore-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"accusantium-inventore_lsat-rc_base": {
"task": "accusantium-inventore_lsat-rc_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "accusantium-inventore-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"amet-ullam_logiqa2_base": {
"task": "amet-ullam_logiqa2_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "amet-ullam-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"amet-ullam_logiqa_base": {
"task": "amet-ullam_logiqa_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "amet-ullam-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"amet-ullam_lsat-ar_base": {
"task": "amet-ullam_lsat-ar_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "amet-ullam-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"amet-ullam_lsat-lr_base": {
"task": "amet-ullam_lsat-lr_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "amet-ullam-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"amet-ullam_lsat-rc_base": {
"task": "amet-ullam_lsat-rc_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "amet-ullam-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"illum-eaque_logiqa2_base": {
"task": "illum-eaque_logiqa2_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "illum-eaque-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"illum-eaque_logiqa_base": {
"task": "illum-eaque_logiqa_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "illum-eaque-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"illum-eaque_lsat-ar_base": {
"task": "illum-eaque_lsat-ar_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "illum-eaque-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"illum-eaque_lsat-lr_base": {
"task": "illum-eaque_lsat-lr_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "illum-eaque-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"illum-eaque_lsat-rc_base": {
"task": "illum-eaque_lsat-rc_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "illum-eaque-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"libero-exercitationem_logiqa2_base": {
"task": "libero-exercitationem_logiqa2_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "libero-exercitationem-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"libero-exercitationem_logiqa_base": {
"task": "libero-exercitationem_logiqa_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "libero-exercitationem-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"libero-exercitationem_lsat-ar_base": {
"task": "libero-exercitationem_lsat-ar_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "libero-exercitationem-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"libero-exercitationem_lsat-lr_base": {
"task": "libero-exercitationem_lsat-lr_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "libero-exercitationem-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"libero-exercitationem_lsat-rc_base": {
"task": "libero-exercitationem_lsat-rc_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "libero-exercitationem-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"magnam-eius_logiqa2_base": {
"task": "magnam-eius_logiqa2_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "magnam-eius-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"magnam-eius_logiqa_base": {
"task": "magnam-eius_logiqa_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "magnam-eius-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"magnam-eius_lsat-ar_base": {
"task": "magnam-eius_lsat-ar_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "magnam-eius-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"magnam-eius_lsat-lr_base": {
"task": "magnam-eius_lsat-lr_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "magnam-eius-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"magnam-eius_lsat-rc_base": {
"task": "magnam-eius_lsat-rc_base",
"group": "logikon-bench",
"dataset_path": "logikon/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "magnam-eius-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"omnis-voluptatibus_logiqa2_base": {
"task": "omnis-voluptatibus_logiqa2_base",
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"task": "omnis-voluptatibus_lsat-ar_base",
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