| { | |
| "results": { | |
| "recusandae-nesciunt-1753_logiqa2_cot": { | |
| "acc,none": 0.3683206106870229, | |
| "acc_stderr,none": 0.012169515043375342, | |
| "alias": "recusandae-nesciunt-1753_logiqa2_cot" | |
| }, | |
| "recusandae-nesciunt-1753_logiqa_cot": { | |
| "acc,none": 0.33865814696485624, | |
| "acc_stderr,none": 0.018930137092337717, | |
| "alias": "recusandae-nesciunt-1753_logiqa_cot" | |
| }, | |
| "recusandae-nesciunt-1753_lsat-ar_cot": { | |
| "acc,none": 0.21304347826086956, | |
| "acc_stderr,none": 0.02705775438993618, | |
| "alias": "recusandae-nesciunt-1753_lsat-ar_cot" | |
| }, | |
| "recusandae-nesciunt-1753_lsat-lr_cot": { | |
| "acc,none": 0.3411764705882353, | |
| "acc_stderr,none": 0.02101431294934919, | |
| "alias": "recusandae-nesciunt-1753_lsat-lr_cot" | |
| }, | |
| "recusandae-nesciunt-1753_lsat-rc_cot": { | |
| "acc,none": 0.42379182156133827, | |
| "acc_stderr,none": 0.030185515550116913, | |
| "alias": "recusandae-nesciunt-1753_lsat-rc_cot" | |
| } | |
| }, | |
| "configs": { | |
| "recusandae-nesciunt-1753_logiqa2_cot": { | |
| "task": "recusandae-nesciunt-1753_logiqa2_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "recusandae-nesciunt-1753-logiqa2/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "recusandae-nesciunt-1753_logiqa_cot": { | |
| "task": "recusandae-nesciunt-1753_logiqa_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "recusandae-nesciunt-1753-logiqa/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "recusandae-nesciunt-1753_lsat-ar_cot": { | |
| "task": "recusandae-nesciunt-1753_lsat-ar_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "recusandae-nesciunt-1753-lsat-ar/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "recusandae-nesciunt-1753_lsat-lr_cot": { | |
| "task": "recusandae-nesciunt-1753_lsat-lr_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "recusandae-nesciunt-1753-lsat-lr/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "recusandae-nesciunt-1753_lsat-rc_cot": { | |
| "task": "recusandae-nesciunt-1753_lsat-rc_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "recusandae-nesciunt-1753-lsat-rc/test-00000-of-00001.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
| "recusandae-nesciunt-1753_logiqa2_cot": 0.0, | |
| "recusandae-nesciunt-1753_logiqa_cot": 0.0, | |
| "recusandae-nesciunt-1753_lsat-ar_cot": 0.0, | |
| "recusandae-nesciunt-1753_lsat-lr_cot": 0.0, | |
| "recusandae-nesciunt-1753_lsat-rc_cot": 0.0 | |
| }, | |
| "n-shot": { | |
| "recusandae-nesciunt-1753_logiqa2_cot": 0, | |
| "recusandae-nesciunt-1753_logiqa_cot": 0, | |
| "recusandae-nesciunt-1753_lsat-ar_cot": 0, | |
| "recusandae-nesciunt-1753_lsat-lr_cot": 0, | |
| "recusandae-nesciunt-1753_lsat-rc_cot": 0 | |
| }, | |
| "config": { | |
| "model": "vllm", | |
| "model_args": "pretrained=openbmb/Eurus-7b-kto,revision=main,dtype=bfloat16,tensor_parallel_size=1,gpu_memory_utilization=0.7,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": "741db1c" | |
| } |