{
  "results": {
    "unde-laudantium_lsat-rc_base": {
      "acc,none": 0.30111524163568776,
      "acc_stderr,none": 0.028022169587612226,
      "alias": "unde-laudantium_lsat-rc_base"
    },
    "unde-laudantium_lsat-lr_base": {
      "acc,none": 0.23529411764705882,
      "acc_stderr,none": 0.018801558887410304,
      "alias": "unde-laudantium_lsat-lr_base"
    },
    "unde-laudantium_lsat-ar_base": {
      "acc,none": 0.20869565217391303,
      "acc_stderr,none": 0.026854108265439675,
      "alias": "unde-laudantium_lsat-ar_base"
    },
    "unde-laudantium_logiqa_base": {
      "acc,none": 0.26996805111821087,
      "acc_stderr,none": 0.017757716181700637,
      "alias": "unde-laudantium_logiqa_base"
    },
    "unde-laudantium_logiqa2_base": {
      "acc,none": 0.3256997455470738,
      "acc_stderr,none": 0.011823533300939599,
      "alias": "unde-laudantium_logiqa2_base"
    },
    "temporibus-illo_lsat-rc_base": {
      "acc,none": 0.26394052044609667,
      "acc_stderr,none": 0.02692415564390256,
      "alias": "temporibus-illo_lsat-rc_base"
    },
    "temporibus-illo_lsat-lr_base": {
      "acc,none": 0.21568627450980393,
      "acc_stderr,none": 0.018230445049830818,
      "alias": "temporibus-illo_lsat-lr_base"
    },
    "temporibus-illo_lsat-ar_base": {
      "acc,none": 0.1826086956521739,
      "acc_stderr,none": 0.025530421952734174,
      "alias": "temporibus-illo_lsat-ar_base"
    },
    "temporibus-illo_logiqa_base": {
      "acc,none": 0.26996805111821087,
      "acc_stderr,none": 0.017757716181700637,
      "alias": "temporibus-illo_logiqa_base"
    },
    "temporibus-illo_logiqa2_base": {
      "acc,none": 0.3187022900763359,
      "acc_stderr,none": 0.011756362373408389,
      "alias": "temporibus-illo_logiqa2_base"
    },
    "quo-non_lsat-rc_base": {
      "acc,none": 0.24907063197026022,
      "acc_stderr,none": 0.02641760298057974,
      "alias": "quo-non_lsat-rc_base"
    },
    "quo-non_lsat-lr_base": {
      "acc,none": 0.2411764705882353,
      "acc_stderr,none": 0.018961774215004727,
      "alias": "quo-non_lsat-lr_base"
    },
    "quo-non_lsat-ar_base": {
      "acc,none": 0.20434782608695654,
      "acc_stderr,none": 0.026645808150011344,
      "alias": "quo-non_lsat-ar_base"
    },
    "quo-non_logiqa_base": {
      "acc,none": 0.25878594249201275,
      "acc_stderr,none": 0.01751871129783383,
      "alias": "quo-non_logiqa_base"
    },
    "quo-non_logiqa2_base": {
      "acc,none": 0.30279898218829515,
      "acc_stderr,none": 0.011592260158888737,
      "alias": "quo-non_logiqa2_base"
    },
    "magni-excepturi_lsat-rc_base": {
      "acc,none": 0.26022304832713755,
      "acc_stderr,none": 0.02680130130545777,
      "alias": "magni-excepturi_lsat-rc_base"
    },
    "magni-excepturi_lsat-lr_base": {
      "acc,none": 0.22745098039215686,
      "acc_stderr,none": 0.018580099622603333,
      "alias": "magni-excepturi_lsat-lr_base"
    },
    "magni-excepturi_lsat-ar_base": {
      "acc,none": 0.17391304347826086,
      "acc_stderr,none": 0.02504731738604972,
      "alias": "magni-excepturi_lsat-ar_base"
    },
    "magni-excepturi_logiqa_base": {
      "acc,none": 0.25878594249201275,
      "acc_stderr,none": 0.01751871129783383,
      "alias": "magni-excepturi_logiqa_base"
    },
    "magni-excepturi_logiqa2_base": {
      "acc,none": 0.30725190839694655,
      "acc_stderr,none": 0.011639836259579924,
      "alias": "magni-excepturi_logiqa2_base"
    },
    "laboriosam-numquam_lsat-rc_base": {
      "acc,none": 0.27137546468401486,
      "acc_stderr,none": 0.027162503089239523,
      "alias": "laboriosam-numquam_lsat-rc_base"
    },
    "laboriosam-numquam_lsat-lr_base": {
      "acc,none": 0.21372549019607842,
      "acc_stderr,none": 0.01817006027631824,
      "alias": "laboriosam-numquam_lsat-lr_base"
    },
    "laboriosam-numquam_lsat-ar_base": {
      "acc,none": 0.21304347826086956,
      "acc_stderr,none": 0.027057754389936177,
      "alias": "laboriosam-numquam_lsat-ar_base"
    },
    "laboriosam-numquam_logiqa_base": {
      "acc,none": 0.25559105431309903,
      "acc_stderr,none": 0.01744771697469749,
      "alias": "laboriosam-numquam_logiqa_base"
    },
    "laboriosam-numquam_logiqa2_base": {
      "acc,none": 0.30725190839694655,
      "acc_stderr,none": 0.011639836259579922,
      "alias": "laboriosam-numquam_logiqa2_base"
    },
    "dolore-possimus_lsat-rc_base": {
      "acc,none": 0.2862453531598513,
      "acc_stderr,none": 0.027610628966374826,
      "alias": "dolore-possimus_lsat-rc_base"
    },
    "dolore-possimus_lsat-lr_base": {
      "acc,none": 0.2196078431372549,
      "acc_stderr,none": 0.01834938361142324,
      "alias": "dolore-possimus_lsat-lr_base"
    },
    "dolore-possimus_lsat-ar_base": {
      "acc,none": 0.2217391304347826,
      "acc_stderr,none": 0.027451496604058916,
      "alias": "dolore-possimus_lsat-ar_base"
    },
    "dolore-possimus_logiqa_base": {
      "acc,none": 0.2763578274760383,
      "acc_stderr,none": 0.01788783625456192,
      "alias": "dolore-possimus_logiqa_base"
    },
    "dolore-possimus_logiqa2_base": {
      "acc,none": 0.2989821882951654,
      "acc_stderr,none": 0.011550454987784068,
      "alias": "dolore-possimus_logiqa2_base"
    }
  },
  "configs": {
    "dolore-possimus_logiqa2_base": {
      "task": "dolore-possimus_logiqa2_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "dolore-possimus-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
      }
    },
    "dolore-possimus_logiqa_base": {
      "task": "dolore-possimus_logiqa_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "dolore-possimus-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
      }
    },
    "dolore-possimus_lsat-ar_base": {
      "task": "dolore-possimus_lsat-ar_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "dolore-possimus-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
      }
    },
    "dolore-possimus_lsat-lr_base": {
      "task": "dolore-possimus_lsat-lr_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "dolore-possimus-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
      }
    },
    "dolore-possimus_lsat-rc_base": {
      "task": "dolore-possimus_lsat-rc_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "dolore-possimus-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
      }
    },
    "laboriosam-numquam_logiqa2_base": {
      "task": "laboriosam-numquam_logiqa2_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "laboriosam-numquam-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
      }
    },
    "laboriosam-numquam_logiqa_base": {
      "task": "laboriosam-numquam_logiqa_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "laboriosam-numquam-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
      }
    },
    "laboriosam-numquam_lsat-ar_base": {
      "task": "laboriosam-numquam_lsat-ar_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "laboriosam-numquam-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
      }
    },
    "laboriosam-numquam_lsat-lr_base": {
      "task": "laboriosam-numquam_lsat-lr_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "laboriosam-numquam-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
      }
    },
    "laboriosam-numquam_lsat-rc_base": {
      "task": "laboriosam-numquam_lsat-rc_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "laboriosam-numquam-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
      }
    },
    "magni-excepturi_logiqa2_base": {
      "task": "magni-excepturi_logiqa2_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "magni-excepturi-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
      }
    },
    "magni-excepturi_logiqa_base": {
      "task": "magni-excepturi_logiqa_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "magni-excepturi-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
      }
    },
    "magni-excepturi_lsat-ar_base": {
      "task": "magni-excepturi_lsat-ar_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "magni-excepturi-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
      }
    },
    "magni-excepturi_lsat-lr_base": {
      "task": "magni-excepturi_lsat-lr_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "magni-excepturi-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
      }
    },
    "magni-excepturi_lsat-rc_base": {
      "task": "magni-excepturi_lsat-rc_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "magni-excepturi-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
      }
    },
    "quo-non_logiqa2_base": {
      "task": "quo-non_logiqa2_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "quo-non-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
      }
    },
    "quo-non_logiqa_base": {
      "task": "quo-non_logiqa_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "quo-non-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
      }
    },
    "quo-non_lsat-ar_base": {
      "task": "quo-non_lsat-ar_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "quo-non-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
      }
    },
    "quo-non_lsat-lr_base": {
      "task": "quo-non_lsat-lr_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "quo-non-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
      }
    },
    "quo-non_lsat-rc_base": {
      "task": "quo-non_lsat-rc_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "quo-non-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
      }
    },
    "temporibus-illo_logiqa2_base": {
      "task": "temporibus-illo_logiqa2_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "temporibus-illo-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
      }
    },
    "temporibus-illo_logiqa_base": {
      "task": "temporibus-illo_logiqa_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "temporibus-illo-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
      }
    },
    "temporibus-illo_lsat-ar_base": {
      "task": "temporibus-illo_lsat-ar_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "temporibus-illo-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
      }
    },
    "temporibus-illo_lsat-lr_base": {
      "task": "temporibus-illo_lsat-lr_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "temporibus-illo-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
      }
    },
    "temporibus-illo_lsat-rc_base": {
      "task": "temporibus-illo_lsat-rc_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "temporibus-illo-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
      }
    },
    "unde-laudantium_logiqa2_base": {
      "task": "unde-laudantium_logiqa2_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "unde-laudantium-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
      }
    },
    "unde-laudantium_logiqa_base": {
      "task": "unde-laudantium_logiqa_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "unde-laudantium-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
      }
    },
    "unde-laudantium_lsat-ar_base": {
      "task": "unde-laudantium_lsat-ar_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "unde-laudantium-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
      }
    },
    "unde-laudantium_lsat-lr_base": {
      "task": "unde-laudantium_lsat-lr_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "unde-laudantium-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
      }
    },
    "unde-laudantium_lsat-rc_base": {
      "task": "unde-laudantium_lsat-rc_base",
      "group": "logikon-bench",
      "dataset_path": "logikon/cot-eval-traces",
      "dataset_kwargs": {
        "data_files": {
          "test": "unde-laudantium-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": {
    "dolore-possimus_logiqa2_base": 0.0,
    "dolore-possimus_logiqa_base": 0.0,
    "dolore-possimus_lsat-ar_base": 0.0,
    "dolore-possimus_lsat-lr_base": 0.0,
    "dolore-possimus_lsat-rc_base": 0.0,
    "laboriosam-numquam_logiqa2_base": 0.0,
    "laboriosam-numquam_logiqa_base": 0.0,
    "laboriosam-numquam_lsat-ar_base": 0.0,
    "laboriosam-numquam_lsat-lr_base": 0.0,
    "laboriosam-numquam_lsat-rc_base": 0.0,
    "magni-excepturi_logiqa2_base": 0.0,
    "magni-excepturi_logiqa_base": 0.0,
    "magni-excepturi_lsat-ar_base": 0.0,
    "magni-excepturi_lsat-lr_base": 0.0,
    "magni-excepturi_lsat-rc_base": 0.0,
    "quo-non_logiqa2_base": 0.0,
    "quo-non_logiqa_base": 0.0,
    "quo-non_lsat-ar_base": 0.0,
    "quo-non_lsat-lr_base": 0.0,
    "quo-non_lsat-rc_base": 0.0,
    "temporibus-illo_logiqa2_base": 0.0,
    "temporibus-illo_logiqa_base": 0.0,
    "temporibus-illo_lsat-ar_base": 0.0,
    "temporibus-illo_lsat-lr_base": 0.0,
    "temporibus-illo_lsat-rc_base": 0.0,
    "unde-laudantium_logiqa2_base": 0.0,
    "unde-laudantium_logiqa_base": 0.0,
    "unde-laudantium_lsat-ar_base": 0.0,
    "unde-laudantium_lsat-lr_base": 0.0,
    "unde-laudantium_lsat-rc_base": 0.0
  },
  "n-shot": {
    "dolore-possimus_logiqa2_base": 0,
    "dolore-possimus_logiqa_base": 0,
    "dolore-possimus_lsat-ar_base": 0,
    "dolore-possimus_lsat-lr_base": 0,
    "dolore-possimus_lsat-rc_base": 0,
    "laboriosam-numquam_logiqa2_base": 0,
    "laboriosam-numquam_logiqa_base": 0,
    "laboriosam-numquam_lsat-ar_base": 0,
    "laboriosam-numquam_lsat-lr_base": 0,
    "laboriosam-numquam_lsat-rc_base": 0,
    "magni-excepturi_logiqa2_base": 0,
    "magni-excepturi_logiqa_base": 0,
    "magni-excepturi_lsat-ar_base": 0,
    "magni-excepturi_lsat-lr_base": 0,
    "magni-excepturi_lsat-rc_base": 0,
    "quo-non_logiqa2_base": 0,
    "quo-non_logiqa_base": 0,
    "quo-non_lsat-ar_base": 0,
    "quo-non_lsat-lr_base": 0,
    "quo-non_lsat-rc_base": 0,
    "temporibus-illo_logiqa2_base": 0,
    "temporibus-illo_logiqa_base": 0,
    "temporibus-illo_lsat-ar_base": 0,
    "temporibus-illo_lsat-lr_base": 0,
    "temporibus-illo_lsat-rc_base": 0,
    "unde-laudantium_logiqa2_base": 0,
    "unde-laudantium_logiqa_base": 0,
    "unde-laudantium_lsat-ar_base": 0,
    "unde-laudantium_lsat-lr_base": 0,
    "unde-laudantium_lsat-rc_base": 0
  },
  "config": {
    "model": "vllm",
    "model_args": "pretrained=mistralai/Mistral-7B-v0.1,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"
}