{ "results": { "veritatis-velit_lsat-rc_base": { "acc,none": 0.37174721189591076, "acc_stderr,none": 0.029520497706913982, "alias": "veritatis-velit_lsat-rc_base" }, "veritatis-velit_lsat-lr_base": { "acc,none": 0.25098039215686274, "acc_stderr,none": 0.0192179738903761, "alias": "veritatis-velit_lsat-lr_base" }, "veritatis-velit_lsat-ar_base": { "acc,none": 0.2217391304347826, "acc_stderr,none": 0.02745149660405891, "alias": "veritatis-velit_lsat-ar_base" }, "veritatis-velit_logiqa_base": { "acc,none": 0.29073482428115016, "acc_stderr,none": 0.018164056209177798, "alias": "veritatis-velit_logiqa_base" }, "veritatis-velit_logiqa2_base": { "acc,none": 0.36704834605597964, "acc_stderr,none": 0.01216070666440455, "alias": "veritatis-velit_logiqa2_base" }, "saepe-fuga_lsat-rc_base": { "acc,none": 0.34944237918215615, "acc_stderr,none": 0.02912482161970039, "alias": "saepe-fuga_lsat-rc_base" }, "saepe-fuga_lsat-lr_base": { "acc,none": 0.2823529411764706, "acc_stderr,none": 0.01995228875819785, "alias": "saepe-fuga_lsat-lr_base" }, "saepe-fuga_lsat-ar_base": { "acc,none": 0.17391304347826086, "acc_stderr,none": 0.025047317386049713, "alias": "saepe-fuga_lsat-ar_base" }, "saepe-fuga_logiqa_base": { "acc,none": 0.30670926517571884, "acc_stderr,none": 0.01844510522956535, "alias": "saepe-fuga_logiqa_base" }, "saepe-fuga_logiqa2_base": { "acc,none": 0.36323155216284986, "acc_stderr,none": 0.012133733683836157, "alias": "saepe-fuga_logiqa2_base" }, "nisi-sunt_lsat-rc_base": { "acc,none": 0.31226765799256506, "acc_stderr,none": 0.028307781204694345, "alias": "nisi-sunt_lsat-rc_base" }, "nisi-sunt_lsat-lr_base": { "acc,none": 0.26862745098039215, "acc_stderr,none": 0.019646519888599705, "alias": "nisi-sunt_lsat-lr_base" }, "nisi-sunt_lsat-ar_base": { "acc,none": 0.20869565217391303, "acc_stderr,none": 0.02685410826543969, "alias": "nisi-sunt_lsat-ar_base" }, "nisi-sunt_logiqa_base": { "acc,none": 0.3003194888178914, "acc_stderr,none": 0.018335874932123606, "alias": "nisi-sunt_logiqa_base" }, "nisi-sunt_logiqa2_base": { "acc,none": 0.37659033078880405, "acc_stderr,none": 0.01222456057756536, "alias": "nisi-sunt_logiqa2_base" }, "laboriosam-molestiae_lsat-rc_base": { "acc,none": 0.3308550185873606, "acc_stderr,none": 0.02874164221560224, "alias": "laboriosam-molestiae_lsat-rc_base" }, "laboriosam-molestiae_lsat-lr_base": { "acc,none": 0.28627450980392155, "acc_stderr,none": 0.020035401617079118, "alias": "laboriosam-molestiae_lsat-lr_base" }, "laboriosam-molestiae_lsat-ar_base": { "acc,none": 0.18695652173913044, "acc_stderr,none": 0.025763772398512335, "alias": "laboriosam-molestiae_lsat-ar_base" }, "laboriosam-molestiae_logiqa_base": { "acc,none": 0.3146964856230032, "acc_stderr,none": 0.018575795328740336, "alias": "laboriosam-molestiae_logiqa_base" }, "laboriosam-molestiae_logiqa2_base": { "acc,none": 0.3746819338422392, "acc_stderr,none": 0.012212196173823686, "alias": "laboriosam-molestiae_logiqa2_base" }, "iste-molestias_lsat-rc_base": { "acc,none": 0.36059479553903345, "acc_stderr,none": 0.029331239329958934, "alias": "iste-molestias_lsat-rc_base" }, "iste-molestias_lsat-lr_base": { "acc,none": 0.2549019607843137, "acc_stderr,none": 0.019316765480532974, "alias": "iste-molestias_lsat-lr_base" }, "iste-molestias_lsat-ar_base": { "acc,none": 0.20869565217391303, "acc_stderr,none": 0.026854108265439654, "alias": "iste-molestias_lsat-ar_base" }, "iste-molestias_logiqa_base": { "acc,none": 0.30670926517571884, "acc_stderr,none": 0.018445105229565353, "alias": "iste-molestias_logiqa_base" }, "iste-molestias_logiqa2_base": { "acc,none": 0.38549618320610685, "acc_stderr,none": 0.01227960059074116, "alias": "iste-molestias_logiqa2_base" }, "eum-saepe_lsat-rc_base": { "acc,none": 0.3643122676579926, "acc_stderr,none": 0.029396215063241374, "alias": "eum-saepe_lsat-rc_base" }, "eum-saepe_lsat-lr_base": { "acc,none": 0.24313725490196078, "acc_stderr,none": 0.01901408485181097, "alias": "eum-saepe_lsat-lr_base" }, "eum-saepe_lsat-ar_base": { "acc,none": 0.19130434782608696, "acc_stderr,none": 0.025991852462828487, "alias": "eum-saepe_lsat-ar_base" }, "eum-saepe_logiqa_base": { "acc,none": 0.3035143769968051, "acc_stderr,none": 0.01839101519560228, "alias": "eum-saepe_logiqa_base" }, "eum-saepe_logiqa2_base": { "acc,none": 0.3651399491094148, "acc_stderr,none": 0.01214732308367413, "alias": "eum-saepe_logiqa2_base" } }, "configs": { "eum-saepe_logiqa2_base": { "task": "eum-saepe_logiqa2_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eum-saepe-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "eum-saepe_logiqa_base": { "task": "eum-saepe_logiqa_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eum-saepe-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "eum-saepe_lsat-ar_base": { "task": "eum-saepe_lsat-ar_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eum-saepe-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "eum-saepe_lsat-lr_base": { "task": "eum-saepe_lsat-lr_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eum-saepe-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "eum-saepe_lsat-rc_base": { "task": "eum-saepe_lsat-rc_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eum-saepe-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "iste-molestias_logiqa2_base": { "task": "iste-molestias_logiqa2_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "iste-molestias-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "iste-molestias_logiqa_base": { "task": "iste-molestias_logiqa_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "iste-molestias-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "iste-molestias_lsat-ar_base": { "task": "iste-molestias_lsat-ar_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "iste-molestias-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "iste-molestias_lsat-lr_base": { "task": "iste-molestias_lsat-lr_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "iste-molestias-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "iste-molestias_lsat-rc_base": { "task": "iste-molestias_lsat-rc_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "iste-molestias-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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-molestiae_logiqa2_base": { "task": "laboriosam-molestiae_logiqa2_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "laboriosam-molestiae-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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-molestiae_logiqa_base": { "task": "laboriosam-molestiae_logiqa_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "laboriosam-molestiae-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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-molestiae_lsat-ar_base": { "task": "laboriosam-molestiae_lsat-ar_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "laboriosam-molestiae-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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-molestiae_lsat-lr_base": { "task": "laboriosam-molestiae_lsat-lr_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "laboriosam-molestiae-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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-molestiae_lsat-rc_base": { "task": "laboriosam-molestiae_lsat-rc_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "laboriosam-molestiae-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "nisi-sunt_logiqa2_base": { "task": "nisi-sunt_logiqa2_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "nisi-sunt-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "nisi-sunt_logiqa_base": { "task": "nisi-sunt_logiqa_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "nisi-sunt-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "nisi-sunt_lsat-ar_base": { "task": "nisi-sunt_lsat-ar_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "nisi-sunt-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "nisi-sunt_lsat-lr_base": { "task": "nisi-sunt_lsat-lr_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "nisi-sunt-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "nisi-sunt_lsat-rc_base": { "task": "nisi-sunt_lsat-rc_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "nisi-sunt-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "saepe-fuga_logiqa2_base": { "task": "saepe-fuga_logiqa2_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "saepe-fuga-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "saepe-fuga_logiqa_base": { "task": "saepe-fuga_logiqa_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "saepe-fuga-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "saepe-fuga_lsat-ar_base": { "task": "saepe-fuga_lsat-ar_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "saepe-fuga-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "saepe-fuga_lsat-lr_base": { "task": "saepe-fuga_lsat-lr_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "saepe-fuga-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "saepe-fuga_lsat-rc_base": { "task": "saepe-fuga_lsat-rc_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "saepe-fuga-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "veritatis-velit_logiqa2_base": { "task": "veritatis-velit_logiqa2_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "veritatis-velit-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "veritatis-velit_logiqa_base": { "task": "veritatis-velit_logiqa_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "veritatis-velit-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "veritatis-velit_lsat-ar_base": { "task": "veritatis-velit_lsat-ar_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "veritatis-velit-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "veritatis-velit_lsat-lr_base": { "task": "veritatis-velit_lsat-lr_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "veritatis-velit-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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 } }, "veritatis-velit_lsat-rc_base": { "task": "veritatis-velit_lsat-rc_base", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "veritatis-velit-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\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": { "eum-saepe_logiqa2_base": 0.0, "eum-saepe_logiqa_base": 0.0, "eum-saepe_lsat-ar_base": 0.0, "eum-saepe_lsat-lr_base": 0.0, "eum-saepe_lsat-rc_base": 0.0, "iste-molestias_logiqa2_base": 0.0, "iste-molestias_logiqa_base": 0.0, "iste-molestias_lsat-ar_base": 0.0, "iste-molestias_lsat-lr_base": 0.0, "iste-molestias_lsat-rc_base": 0.0, "laboriosam-molestiae_logiqa2_base": 0.0, "laboriosam-molestiae_logiqa_base": 0.0, "laboriosam-molestiae_lsat-ar_base": 0.0, "laboriosam-molestiae_lsat-lr_base": 0.0, "laboriosam-molestiae_lsat-rc_base": 0.0, "nisi-sunt_logiqa2_base": 0.0, "nisi-sunt_logiqa_base": 0.0, "nisi-sunt_lsat-ar_base": 0.0, "nisi-sunt_lsat-lr_base": 0.0, "nisi-sunt_lsat-rc_base": 0.0, "saepe-fuga_logiqa2_base": 0.0, "saepe-fuga_logiqa_base": 0.0, "saepe-fuga_lsat-ar_base": 0.0, "saepe-fuga_lsat-lr_base": 0.0, "saepe-fuga_lsat-rc_base": 0.0, "veritatis-velit_logiqa2_base": 0.0, "veritatis-velit_logiqa_base": 0.0, "veritatis-velit_lsat-ar_base": 0.0, "veritatis-velit_lsat-lr_base": 0.0, "veritatis-velit_lsat-rc_base": 0.0 }, "n-shot": { "eum-saepe_logiqa2_base": 0, "eum-saepe_logiqa_base": 0, "eum-saepe_lsat-ar_base": 0, "eum-saepe_lsat-lr_base": 0, "eum-saepe_lsat-rc_base": 0, "iste-molestias_logiqa2_base": 0, "iste-molestias_logiqa_base": 0, "iste-molestias_lsat-ar_base": 0, "iste-molestias_lsat-lr_base": 0, "iste-molestias_lsat-rc_base": 0, "laboriosam-molestiae_logiqa2_base": 0, "laboriosam-molestiae_logiqa_base": 0, "laboriosam-molestiae_lsat-ar_base": 0, "laboriosam-molestiae_lsat-lr_base": 0, "laboriosam-molestiae_lsat-rc_base": 0, "nisi-sunt_logiqa2_base": 0, "nisi-sunt_logiqa_base": 0, "nisi-sunt_lsat-ar_base": 0, "nisi-sunt_lsat-lr_base": 0, "nisi-sunt_lsat-rc_base": 0, "saepe-fuga_logiqa2_base": 0, "saepe-fuga_logiqa_base": 0, "saepe-fuga_lsat-ar_base": 0, "saepe-fuga_lsat-lr_base": 0, "saepe-fuga_lsat-rc_base": 0, "veritatis-velit_logiqa2_base": 0, "veritatis-velit_logiqa_base": 0, "veritatis-velit_lsat-ar_base": 0, "veritatis-velit_lsat-lr_base": 0, "veritatis-velit_lsat-rc_base": 0 }, "config": { "model": "vllm", "model_args": "pretrained=Deci/DeciLM-7B,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" }