{ "results": { "repellendus-laborum_lsat-rc_cot": { "acc,none": 0.39776951672862454, "acc_stderr,none": 0.02989714509220832, "alias": "repellendus-laborum_lsat-rc_cot" }, "repellendus-laborum_lsat-lr_cot": { "acc,none": 0.3215686274509804, "acc_stderr,none": 0.020702886736741085, "alias": "repellendus-laborum_lsat-lr_cot" }, "repellendus-laborum_lsat-ar_cot": { "acc,none": 0.1956521739130435, "acc_stderr,none": 0.026214799709819596, "alias": "repellendus-laborum_lsat-ar_cot" }, "repellendus-laborum_logiqa_cot": { "acc,none": 0.35303514376996803, "acc_stderr,none": 0.019116540734485793, "alias": "repellendus-laborum_logiqa_cot" }, "repellendus-laborum_logiqa2_cot": { "acc,none": 0.38040712468193383, "acc_stderr,none": 0.01224868415939611, "alias": "repellendus-laborum_logiqa2_cot" }, "possimus-voluptate_lsat-rc_cot": { "acc,none": 0.3048327137546468, "acc_stderr,none": 0.02811952967561346, "alias": "possimus-voluptate_lsat-rc_cot" }, "possimus-voluptate_lsat-lr_cot": { "acc,none": 0.2901960784313726, "acc_stderr,none": 0.020116669259866344, "alias": "possimus-voluptate_lsat-lr_cot" }, "possimus-voluptate_lsat-ar_cot": { "acc,none": 0.21304347826086956, "acc_stderr,none": 0.027057754389936194, "alias": "possimus-voluptate_lsat-ar_cot" }, "possimus-voluptate_logiqa_cot": { "acc,none": 0.31309904153354634, "acc_stderr,none": 0.018550171178695694, "alias": "possimus-voluptate_logiqa_cot" }, "possimus-voluptate_logiqa2_cot": { "acc,none": 0.34478371501272265, "acc_stderr,none": 0.011991613472848751, "alias": "possimus-voluptate_logiqa2_cot" }, "maxime-expedita_lsat-rc_cot": { "acc,none": 0.3382899628252788, "acc_stderr,none": 0.028900876908980185, "alias": "maxime-expedita_lsat-rc_cot" }, "maxime-expedita_lsat-lr_cot": { "acc,none": 0.2568627450980392, "acc_stderr,none": 0.019365387229579173, "alias": "maxime-expedita_lsat-lr_cot" }, "maxime-expedita_lsat-ar_cot": { "acc,none": 0.24782608695652175, "acc_stderr,none": 0.02853086259541007, "alias": "maxime-expedita_lsat-ar_cot" }, "maxime-expedita_logiqa_cot": { "acc,none": 0.3083067092651757, "acc_stderr,none": 0.018471759300608265, "alias": "maxime-expedita_logiqa_cot" }, "maxime-expedita_logiqa2_cot": { "acc,none": 0.3237913486005089, "acc_stderr,none": 0.01180551369127738, "alias": "maxime-expedita_logiqa2_cot" }, "eveniet-ea_lsat-rc_cot": { "acc,none": 0.35315985130111527, "acc_stderr,none": 0.029195555959749025, "alias": "eveniet-ea_lsat-rc_cot" }, "eveniet-ea_lsat-lr_cot": { "acc,none": 0.2823529411764706, "acc_stderr,none": 0.01995228875819785, "alias": "eveniet-ea_lsat-lr_cot" }, "eveniet-ea_lsat-ar_cot": { "acc,none": 0.2565217391304348, "acc_stderr,none": 0.028858814315305643, "alias": "eveniet-ea_lsat-ar_cot" }, "eveniet-ea_logiqa_cot": { "acc,none": 0.3226837060702875, "acc_stderr,none": 0.01870011473363866, "alias": "eveniet-ea_logiqa_cot" }, "eveniet-ea_logiqa2_cot": { "acc,none": 0.36323155216284986, "acc_stderr,none": 0.012133733683836153, "alias": "eveniet-ea_logiqa2_cot" }, "distinctio-unde_lsat-rc_cot": { "acc,none": 0.34572490706319703, "acc_stderr,none": 0.029052140190085934, "alias": "distinctio-unde_lsat-rc_cot" }, "distinctio-unde_lsat-lr_cot": { "acc,none": 0.2803921568627451, "acc_stderr,none": 0.01991003317147411, "alias": "distinctio-unde_lsat-lr_cot" }, "distinctio-unde_lsat-ar_cot": { "acc,none": 0.23043478260869565, "acc_stderr,none": 0.027827807522276156, "alias": "distinctio-unde_lsat-ar_cot" }, "distinctio-unde_logiqa_cot": { "acc,none": 0.329073482428115, "acc_stderr,none": 0.018795068527281106, "alias": "distinctio-unde_logiqa_cot" }, "distinctio-unde_logiqa2_cot": { "acc,none": 0.361323155216285, "acc_stderr,none": 0.012119937772570024, "alias": "distinctio-unde_logiqa2_cot" }, "aspernatur-sint_lsat-rc_cot": { "acc,none": 0.32342007434944237, "acc_stderr,none": 0.02857430284450382, "alias": "aspernatur-sint_lsat-rc_cot" }, "aspernatur-sint_lsat-lr_cot": { "acc,none": 0.2901960784313726, "acc_stderr,none": 0.020116669259866347, "alias": "aspernatur-sint_lsat-lr_cot" }, "aspernatur-sint_lsat-ar_cot": { "acc,none": 0.22608695652173913, "acc_stderr,none": 0.02764178570724133, "alias": "aspernatur-sint_lsat-ar_cot" }, "aspernatur-sint_logiqa_cot": { "acc,none": 0.31150159744408945, "acc_stderr,none": 0.01852429117602582, "alias": "aspernatur-sint_logiqa_cot" }, "aspernatur-sint_logiqa2_cot": { "acc,none": 0.35814249363867684, "acc_stderr,none": 0.012096483748969475, "alias": "aspernatur-sint_logiqa2_cot" } }, "configs": { "aspernatur-sint_logiqa2_cot": { "task": "aspernatur-sint_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "aspernatur-sint-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "aspernatur-sint_logiqa_cot": { "task": "aspernatur-sint_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "aspernatur-sint-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "aspernatur-sint_lsat-ar_cot": { "task": "aspernatur-sint_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "aspernatur-sint-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "aspernatur-sint_lsat-lr_cot": { "task": "aspernatur-sint_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "aspernatur-sint-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "aspernatur-sint_lsat-rc_cot": { "task": "aspernatur-sint_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "aspernatur-sint-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "distinctio-unde_logiqa2_cot": { "task": "distinctio-unde_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "distinctio-unde-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "distinctio-unde_logiqa_cot": { "task": "distinctio-unde_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "distinctio-unde-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "distinctio-unde_lsat-ar_cot": { "task": "distinctio-unde_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "distinctio-unde-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "distinctio-unde_lsat-lr_cot": { "task": "distinctio-unde_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "distinctio-unde-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "distinctio-unde_lsat-rc_cot": { "task": "distinctio-unde_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "distinctio-unde-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "eveniet-ea_logiqa2_cot": { "task": "eveniet-ea_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eveniet-ea-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "eveniet-ea_logiqa_cot": { "task": "eveniet-ea_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eveniet-ea-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "eveniet-ea_lsat-ar_cot": { "task": "eveniet-ea_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eveniet-ea-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "eveniet-ea_lsat-lr_cot": { "task": "eveniet-ea_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eveniet-ea-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "eveniet-ea_lsat-rc_cot": { "task": "eveniet-ea_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eveniet-ea-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "maxime-expedita_logiqa2_cot": { "task": "maxime-expedita_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "maxime-expedita-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "maxime-expedita_logiqa_cot": { "task": "maxime-expedita_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "maxime-expedita-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "maxime-expedita_lsat-ar_cot": { "task": "maxime-expedita_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "maxime-expedita-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "maxime-expedita_lsat-lr_cot": { "task": "maxime-expedita_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "maxime-expedita-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "maxime-expedita_lsat-rc_cot": { "task": "maxime-expedita_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "maxime-expedita-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "possimus-voluptate_logiqa2_cot": { "task": "possimus-voluptate_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "possimus-voluptate-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "possimus-voluptate_logiqa_cot": { "task": "possimus-voluptate_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "possimus-voluptate-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "possimus-voluptate_lsat-ar_cot": { "task": "possimus-voluptate_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "possimus-voluptate-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "possimus-voluptate_lsat-lr_cot": { "task": "possimus-voluptate_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "possimus-voluptate-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "possimus-voluptate_lsat-rc_cot": { "task": "possimus-voluptate_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "possimus-voluptate-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "repellendus-laborum_logiqa2_cot": { "task": "repellendus-laborum_logiqa2_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "repellendus-laborum-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "repellendus-laborum_logiqa_cot": { "task": "repellendus-laborum_logiqa_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "repellendus-laborum-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "repellendus-laborum_lsat-ar_cot": { "task": "repellendus-laborum_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "repellendus-laborum-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "repellendus-laborum_lsat-lr_cot": { "task": "repellendus-laborum_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "repellendus-laborum-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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 } }, "repellendus-laborum_lsat-rc_cot": { "task": "repellendus-laborum_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "logikon/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "repellendus-laborum-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: \n \n Question: \n A. \n B. \n C. \n D. \n [E. ]\n \n [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": { "aspernatur-sint_logiqa2_cot": 0.0, "aspernatur-sint_logiqa_cot": 0.0, "aspernatur-sint_lsat-ar_cot": 0.0, "aspernatur-sint_lsat-lr_cot": 0.0, "aspernatur-sint_lsat-rc_cot": 0.0, "distinctio-unde_logiqa2_cot": 0.0, "distinctio-unde_logiqa_cot": 0.0, "distinctio-unde_lsat-ar_cot": 0.0, "distinctio-unde_lsat-lr_cot": 0.0, "distinctio-unde_lsat-rc_cot": 0.0, "eveniet-ea_logiqa2_cot": 0.0, "eveniet-ea_logiqa_cot": 0.0, "eveniet-ea_lsat-ar_cot": 0.0, "eveniet-ea_lsat-lr_cot": 0.0, "eveniet-ea_lsat-rc_cot": 0.0, "maxime-expedita_logiqa2_cot": 0.0, "maxime-expedita_logiqa_cot": 0.0, "maxime-expedita_lsat-ar_cot": 0.0, "maxime-expedita_lsat-lr_cot": 0.0, "maxime-expedita_lsat-rc_cot": 0.0, "possimus-voluptate_logiqa2_cot": 0.0, "possimus-voluptate_logiqa_cot": 0.0, "possimus-voluptate_lsat-ar_cot": 0.0, "possimus-voluptate_lsat-lr_cot": 0.0, "possimus-voluptate_lsat-rc_cot": 0.0, "repellendus-laborum_logiqa2_cot": 0.0, "repellendus-laborum_logiqa_cot": 0.0, "repellendus-laborum_lsat-ar_cot": 0.0, "repellendus-laborum_lsat-lr_cot": 0.0, "repellendus-laborum_lsat-rc_cot": 0.0 }, "n-shot": { "aspernatur-sint_logiqa2_cot": 0, "aspernatur-sint_logiqa_cot": 0, "aspernatur-sint_lsat-ar_cot": 0, "aspernatur-sint_lsat-lr_cot": 0, "aspernatur-sint_lsat-rc_cot": 0, "distinctio-unde_logiqa2_cot": 0, "distinctio-unde_logiqa_cot": 0, "distinctio-unde_lsat-ar_cot": 0, "distinctio-unde_lsat-lr_cot": 0, "distinctio-unde_lsat-rc_cot": 0, "eveniet-ea_logiqa2_cot": 0, "eveniet-ea_logiqa_cot": 0, "eveniet-ea_lsat-ar_cot": 0, "eveniet-ea_lsat-lr_cot": 0, "eveniet-ea_lsat-rc_cot": 0, "maxime-expedita_logiqa2_cot": 0, "maxime-expedita_logiqa_cot": 0, "maxime-expedita_lsat-ar_cot": 0, "maxime-expedita_lsat-lr_cot": 0, "maxime-expedita_lsat-rc_cot": 0, "possimus-voluptate_logiqa2_cot": 0, "possimus-voluptate_logiqa_cot": 0, "possimus-voluptate_lsat-ar_cot": 0, "possimus-voluptate_lsat-lr_cot": 0, "possimus-voluptate_lsat-rc_cot": 0, "repellendus-laborum_logiqa2_cot": 0, "repellendus-laborum_logiqa_cot": 0, "repellendus-laborum_lsat-ar_cot": 0, "repellendus-laborum_lsat-lr_cot": 0, "repellendus-laborum_lsat-rc_cot": 0 }, "config": { "model": "vllm", "model_args": "pretrained=microsoft/phi-2,revision=main,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.9,trust_remote_code=true,max_length=2048", "batch_size": "auto", "batch_sizes": [], "device": null, "use_cache": null, "limit": null, "bootstrap_iters": 100000, "gen_kwargs": null }, "git_hash": "3d5b980" }