{ "results": { "eligendi-explicabo-7111_logiqa2_base": { "acc,none": 0.34923664122137404, "acc_stderr,none": 0.012027721259976698, "alias": "eligendi-explicabo-7111_logiqa2_base" }, "eligendi-explicabo-7111_logiqa_base": { "acc,none": 0.329073482428115, "acc_stderr,none": 0.0187950685272811, "alias": "eligendi-explicabo-7111_logiqa_base" }, "eligendi-explicabo-7111_lsat-ar_base": { "acc,none": 0.19130434782608696, "acc_stderr,none": 0.025991852462828483, "alias": "eligendi-explicabo-7111_lsat-ar_base" }, "eligendi-explicabo-7111_lsat-lr_base": { "acc,none": 0.2411764705882353, "acc_stderr,none": 0.018961774215004723, "alias": "eligendi-explicabo-7111_lsat-lr_base" }, "eligendi-explicabo-7111_lsat-rc_base": { "acc,none": 0.34944237918215615, "acc_stderr,none": 0.02912482161970039, "alias": "eligendi-explicabo-7111_lsat-rc_base" } }, "configs": { "eligendi-explicabo-7111_logiqa2_base": { "task": "eligendi-explicabo-7111_logiqa2_base", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eligendi-explicabo-7111-logiqa2/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "eligendi-explicabo-7111_logiqa_base": { "task": "eligendi-explicabo-7111_logiqa_base", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eligendi-explicabo-7111-logiqa/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "eligendi-explicabo-7111_lsat-ar_base": { "task": "eligendi-explicabo-7111_lsat-ar_base", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eligendi-explicabo-7111-lsat-ar/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "eligendi-explicabo-7111_lsat-lr_base": { "task": "eligendi-explicabo-7111_lsat-lr_base", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eligendi-explicabo-7111-lsat-lr/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "eligendi-explicabo-7111_lsat-rc_base": { "task": "eligendi-explicabo-7111_lsat-rc_base", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "eligendi-explicabo-7111-lsat-rc/test-00000-of-00001.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } } }, "versions": { "eligendi-explicabo-7111_logiqa2_base": 0.0, "eligendi-explicabo-7111_logiqa_base": 0.0, "eligendi-explicabo-7111_lsat-ar_base": 0.0, "eligendi-explicabo-7111_lsat-lr_base": 0.0, "eligendi-explicabo-7111_lsat-rc_base": 0.0 }, "n-shot": { "eligendi-explicabo-7111_logiqa2_base": 0, "eligendi-explicabo-7111_logiqa_base": 0, "eligendi-explicabo-7111_lsat-ar_base": 0, "eligendi-explicabo-7111_lsat-lr_base": 0, "eligendi-explicabo-7111_lsat-rc_base": 0 }, "config": { "model": "vllm", "model_args": "pretrained=mistralai/Mixtral-8x7B-v0.1,revision=main,dtype=bfloat16,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": "741db1c" }