{ "results": { "ex-quia-4774_logiqa2_cot": { "acc,none": 0.4211195928753181, "acc_stderr,none": 0.012456873570626794, "alias": "ex-quia-4774_logiqa2_cot" }, "ex-quia-4774_logiqa_cot": { "acc,none": 0.3242811501597444, "acc_stderr,none": 0.018724225412478597, "alias": "ex-quia-4774_logiqa_cot" }, "ex-quia-4774_lsat-ar_cot": { "acc,none": 0.20869565217391303, "acc_stderr,none": 0.026854108265439654, "alias": "ex-quia-4774_lsat-ar_cot" }, "ex-quia-4774_lsat-lr_cot": { "acc,none": 0.45294117647058824, "acc_stderr,none": 0.02206373457408461, "alias": "ex-quia-4774_lsat-lr_cot" }, "ex-quia-4774_lsat-rc_cot": { "acc,none": 0.5390334572490706, "acc_stderr,none": 0.030449149512372414, "alias": "ex-quia-4774_lsat-rc_cot" } }, "configs": { "ex-quia-4774_logiqa2_cot": { "task": "ex-quia-4774_logiqa2_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "ex-quia-4774-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 } }, "ex-quia-4774_logiqa_cot": { "task": "ex-quia-4774_logiqa_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "ex-quia-4774-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 } }, "ex-quia-4774_lsat-ar_cot": { "task": "ex-quia-4774_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "ex-quia-4774-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 } }, "ex-quia-4774_lsat-lr_cot": { "task": "ex-quia-4774_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "ex-quia-4774-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 } }, "ex-quia-4774_lsat-rc_cot": { "task": "ex-quia-4774_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "ex-quia-4774-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": { "ex-quia-4774_logiqa2_cot": 0.0, "ex-quia-4774_logiqa_cot": 0.0, "ex-quia-4774_lsat-ar_cot": 0.0, "ex-quia-4774_lsat-lr_cot": 0.0, "ex-quia-4774_lsat-rc_cot": 0.0 }, "n-shot": { "ex-quia-4774_logiqa2_cot": 0, "ex-quia-4774_logiqa_cot": 0, "ex-quia-4774_lsat-ar_cot": 0, "ex-quia-4774_lsat-lr_cot": 0, "ex-quia-4774_lsat-rc_cot": 0 }, "config": { "model": "vllm", "model_args": "pretrained=meta-llama/Llama-2-70b-chat-hf,revision=main,dtype=float16,tensor_parallel_size=8,gpu_memory_utilization=0.8,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": "fa353d4" }