updated results for bloom

lm-eval-output/bigscience/bloom-7b1/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json

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lm-eval-output/bigscience/bloom-7b1/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json

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lm-eval-output/bigscience/bloom-7b1/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log

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lm-eval-output/bigscience/bloom-7b1/arithmetic/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json

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lm-eval-output/bigscience/bloom-7b1/arithmetic/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log

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+    "blimp_principle_A_domain_3": {
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+      "acc_stderr,none": 0.01411109928825958,
+      "alias": " - blimp_principle_A_domain_3"
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+    "blimp_principle_A_reconstruction": {
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+      "acc_stderr,none": 0.014998131348402704,
+      "alias": " - blimp_principle_A_reconstruction"
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+    "blimp_regular_plural_subject_verb_agreement_1": {
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+      "acc_stderr,none": 0.008384169266796386,
+      "alias": " - blimp_regular_plural_subject_verb_agreement_1"
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+      "acc_stderr,none": 0.00848457353011858,
+      "alias": " - blimp_regular_plural_subject_verb_agreement_2"
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+    "blimp_sentential_negation_npi_licensor_present": {
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+      "acc_stderr,none": 0.0042063872496114875,
+      "alias": " - blimp_sentential_negation_npi_licensor_present"
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+      "acc_stderr,none": 0.013232501619085332,
+      "alias": " - blimp_sentential_negation_npi_scope"
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+      "acc_stderr,none": 0.01581547119529269,
+      "alias": " - blimp_sentential_subject_island"
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+      "acc_stderr,none": 0.007024624213817135,
+      "alias": " - blimp_superlative_quantifiers_1"
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+      "acc_stderr,none": 0.006273624021118745,
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+      "acc_stderr,none": 0.011328165223341678,
+      "alias": " - blimp_transitive"
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+      "acc_stderr,none": 0.011772110370812196,
+      "alias": " - blimp_wh_island"
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+    "blimp_wh_questions_object_gap": {
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+      "acc_stderr,none": 0.010093407594904617,
+      "alias": " - blimp_wh_questions_object_gap"
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+    "blimp_wh_questions_subject_gap": {
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+      "acc_stderr,none": 0.0070881056172464405,
+      "alias": " - blimp_wh_questions_subject_gap"
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+      "acc_stderr,none": 0.009144376393151089,
+      "alias": " - blimp_wh_questions_subject_gap_long_distance"
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+    "blimp_wh_vs_that_no_gap": {
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+      "acc_stderr,none": 0.004640855259274701,
+      "alias": " - blimp_wh_vs_that_no_gap"
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+      "acc_stderr,none": 0.005814534272734956,
+      "alias": " - blimp_wh_vs_that_no_gap_long_distance"
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+    "blimp_wh_vs_that_with_gap": {
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+      "acc_stderr,none": 0.015599819048769618,
+      "alias": " - blimp_wh_vs_that_with_gap"
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+    "blimp_wh_vs_that_with_gap_long_distance": {
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+      "acc_stderr,none": 0.015110404505648666,
+      "alias": " - blimp_wh_vs_that_with_gap_long_distance"
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+      "acc_stderr,none": 0.1473780989902262,
+      "alias": "blimp"
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+    "blimp_determiner_noun_agreement_with_adj_2": {
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+    "blimp_determiner_noun_agreement_with_adj_irregular_2": {
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+    "blimp_distractor_agreement_relational_noun": {
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+      "acc,none": 0.2916666666666667,
+      "acc_stderr,none": 0.09477598811252415,
+      "acc_norm,none": 0.2916666666666667,
+      "acc_norm_stderr,none": 0.09477598811252415,
+      "alias": " - ceval-valid_metrology_engineer"
+    },
+    "ceval-valid_middle_school_biology": {
+      "acc,none": 0.19047619047619047,
+      "acc_stderr,none": 0.08780518530755133,
+      "acc_norm,none": 0.19047619047619047,
+      "acc_norm_stderr,none": 0.08780518530755133,
+      "alias": " - ceval-valid_middle_school_biology"
+    },
+    "ceval-valid_middle_school_chemistry": {
+      "acc,none": 0.4,
+      "acc_stderr,none": 0.11239029738980327,
+      "acc_norm,none": 0.4,
+      "acc_norm_stderr,none": 0.11239029738980327,
+      "alias": " - ceval-valid_middle_school_chemistry"
+    },
+    "ceval-valid_middle_school_geography": {
+      "acc,none": 0.4166666666666667,
+      "acc_stderr,none": 0.1486470975026408,
+      "acc_norm,none": 0.4166666666666667,
+      "acc_norm_stderr,none": 0.1486470975026408,
+      "alias": " - ceval-valid_middle_school_geography"
+    },
+    "ceval-valid_middle_school_history": {
+      "acc,none": 0.4090909090909091,
+      "acc_stderr,none": 0.10729033533674223,
+      "acc_norm,none": 0.4090909090909091,
+      "acc_norm_stderr,none": 0.10729033533674223,
+      "alias": " - ceval-valid_middle_school_history"
+    },
+    "ceval-valid_middle_school_mathematics": {
+      "acc,none": 0.2631578947368421,
+      "acc_stderr,none": 0.10379087338771256,
+      "acc_norm,none": 0.2631578947368421,
+      "acc_norm_stderr,none": 0.10379087338771256,
+      "alias": " - ceval-valid_middle_school_mathematics"
+    },
+    "ceval-valid_middle_school_physics": {
+      "acc,none": 0.21052631578947367,
+      "acc_stderr,none": 0.0960916767552923,
+      "acc_norm,none": 0.21052631578947367,
+      "acc_norm_stderr,none": 0.0960916767552923,
+      "alias": " - ceval-valid_middle_school_physics"
+    },
+    "ceval-valid_middle_school_politics": {
+      "acc,none": 0.38095238095238093,
+      "acc_stderr,none": 0.10858813572372741,
+      "acc_norm,none": 0.38095238095238093,
+      "acc_norm_stderr,none": 0.10858813572372741,
+      "alias": " - ceval-valid_middle_school_politics"
+    },
+    "ceval-valid_modern_chinese_history": {
+      "acc,none": 0.5217391304347826,
+      "acc_stderr,none": 0.10649955403405124,
+      "acc_norm,none": 0.5217391304347826,
+      "acc_norm_stderr,none": 0.10649955403405124,
+      "alias": " - ceval-valid_modern_chinese_history"
+    },
+    "ceval-valid_operating_system": {
+      "acc,none": 0.47368421052631576,
+      "acc_stderr,none": 0.11768778828946262,
+      "acc_norm,none": 0.47368421052631576,
+      "acc_norm_stderr,none": 0.11768778828946262,
+      "alias": " - ceval-valid_operating_system"
+    },
+    "ceval-valid_physician": {
+      "acc,none": 0.30612244897959184,
+      "acc_stderr,none": 0.06652247352247599,
+      "acc_norm,none": 0.30612244897959184,
+      "acc_norm_stderr,none": 0.06652247352247599,
+      "alias": " - ceval-valid_physician"
+    },
+    "ceval-valid_plant_protection": {
+      "acc,none": 0.22727272727272727,
+      "acc_stderr,none": 0.0914486154730632,
+      "acc_norm,none": 0.22727272727272727,
+      "acc_norm_stderr,none": 0.0914486154730632,
+      "alias": " - ceval-valid_plant_protection"
+    },
+    "ceval-valid_probability_and_statistics": {
+      "acc,none": 0.2777777777777778,
+      "acc_stderr,none": 0.1086324845659782,
+      "acc_norm,none": 0.2777777777777778,
+      "acc_norm_stderr,none": 0.1086324845659782,
+      "alias": " - ceval-valid_probability_and_statistics"
+    },
+    "ceval-valid_professional_tour_guide": {
+      "acc,none": 0.20689655172413793,
+      "acc_stderr,none": 0.07655305550699536,
+      "acc_norm,none": 0.20689655172413793,
+      "acc_norm_stderr,none": 0.07655305550699536,
+      "alias": " - ceval-valid_professional_tour_guide"
+    },
+    "ceval-valid_sports_science": {
+      "acc,none": 0.2631578947368421,
+      "acc_stderr,none": 0.10379087338771256,
+      "acc_norm,none": 0.2631578947368421,
+      "acc_norm_stderr,none": 0.10379087338771256,
+      "alias": " - ceval-valid_sports_science"
+    },
+    "ceval-valid_tax_accountant": {
+      "acc,none": 0.3469387755102041,
+      "acc_stderr,none": 0.06870411522695291,
+      "acc_norm,none": 0.3469387755102041,
+      "acc_norm_stderr,none": 0.06870411522695291,
+      "alias": " - ceval-valid_tax_accountant"
+    },
+    "ceval-valid_teacher_qualification": {
+      "acc,none": 0.22727272727272727,
+      "acc_stderr,none": 0.06390760676613884,
+      "acc_norm,none": 0.22727272727272727,
+      "acc_norm_stderr,none": 0.06390760676613884,
+      "alias": " - ceval-valid_teacher_qualification"
+    },
+    "ceval-valid_urban_and_rural_planner": {
+      "acc,none": 0.2391304347826087,
+      "acc_stderr,none": 0.06358669845936323,
+      "acc_norm,none": 0.2391304347826087,
+      "acc_norm_stderr,none": 0.06358669845936323,
+      "alias": " - ceval-valid_urban_and_rural_planner"
+    },
+    "ceval-valid_veterinary_medicine": {
+      "acc,none": 0.21739130434782608,
+      "acc_stderr,none": 0.08793911249520549,
+      "acc_norm,none": 0.21739130434782608,
+      "acc_norm_stderr,none": 0.08793911249520549,
+      "alias": " - ceval-valid_veterinary_medicine"
+    }
+  },
+  "groups": {
+    "ceval-valid": {
+      "acc,none": 0.2600297176820208,
+      "acc_stderr,none": 0.11835357984205985,
+      "acc_norm,none": 0.2600297176820208,
+      "acc_norm_stderr,none": 0.11835357984205985,
+      "alias": "ceval-valid"
+    }
+  },
+  "configs": {
+    "ceval-valid_accountant": {
+      "task": "ceval-valid_accountant",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "accountant",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于注册会计师的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_advanced_mathematics": {
+      "task": "ceval-valid_advanced_mathematics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "advanced_mathematics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高等数学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_art_studies": {
+      "task": "ceval-valid_art_studies",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "art_studies",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于艺术学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_basic_medicine": {
+      "task": "ceval-valid_basic_medicine",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "basic_medicine",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于基础医学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_business_administration": {
+      "task": "ceval-valid_business_administration",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "business_administration",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于工商管理的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_chinese_language_and_literature": {
+      "task": "ceval-valid_chinese_language_and_literature",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "chinese_language_and_literature",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于中国语言文学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_civil_servant": {
+      "task": "ceval-valid_civil_servant",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "civil_servant",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于公务员的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_clinical_medicine": {
+      "task": "ceval-valid_clinical_medicine",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "clinical_medicine",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于临床医学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_college_chemistry": {
+      "task": "ceval-valid_college_chemistry",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "college_chemistry",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于大学化学的���项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_college_economics": {
+      "task": "ceval-valid_college_economics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "college_economics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于大学经济学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_college_physics": {
+      "task": "ceval-valid_college_physics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "college_physics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于大学物理的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_college_programming": {
+      "task": "ceval-valid_college_programming",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "college_programming",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于大学编程的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_computer_architecture": {
+      "task": "ceval-valid_computer_architecture",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "computer_architecture",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于计算机组成的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_computer_network": {
+      "task": "ceval-valid_computer_network",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "computer_network",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于计算机网络的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_discrete_mathematics": {
+      "task": "ceval-valid_discrete_mathematics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "discrete_mathematics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于离散数学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_education_science": {
+      "task": "ceval-valid_education_science",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "education_science",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于教育学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_electrical_engineer": {
+      "task": "ceval-valid_electrical_engineer",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "electrical_engineer",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于注册电气工程师的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_environmental_impact_assessment_engineer": {
+      "task": "ceval-valid_environmental_impact_assessment_engineer",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "environmental_impact_assessment_engineer",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于环境影响评价工程师的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_fire_engineer": {
+      "task": "ceval-valid_fire_engineer",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "fire_engineer",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于注册消防工程师的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_biology": {
+      "task": "ceval-valid_high_school_biology",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_biology",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中生物的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_chemistry": {
+      "task": "ceval-valid_high_school_chemistry",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_chemistry",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中化学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_chinese": {
+      "task": "ceval-valid_high_school_chinese",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_chinese",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中语文的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_geography": {
+      "task": "ceval-valid_high_school_geography",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_geography",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中地理的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_history": {
+      "task": "ceval-valid_high_school_history",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_history",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中历史的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_mathematics": {
+      "task": "ceval-valid_high_school_mathematics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_mathematics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中数学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_physics": {
+      "task": "ceval-valid_high_school_physics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_physics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中物理的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_high_school_politics": {
+      "task": "ceval-valid_high_school_politics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "high_school_politics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于高中政治的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_ideological_and_moral_cultivation": {
+      "task": "ceval-valid_ideological_and_moral_cultivation",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "ideological_and_moral_cultivation",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于思想道德修养与法律基础的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_law": {
+      "task": "ceval-valid_law",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "law",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于法学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_legal_professional": {
+      "task": "ceval-valid_legal_professional",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "legal_professional",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于法律职业资格的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_logic": {
+      "task": "ceval-valid_logic",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "logic",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于逻辑学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_mao_zedong_thought": {
+      "task": "ceval-valid_mao_zedong_thought",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "mao_zedong_thought",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于毛泽东思想和中国特色社会主义理论体系概论的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_marxism": {
+      "task": "ceval-valid_marxism",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "marxism",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于马克思主义基本原理的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_metrology_engineer": {
+      "task": "ceval-valid_metrology_engineer",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "metrology_engineer",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于注册计量师的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_middle_school_biology": {
+      "task": "ceval-valid_middle_school_biology",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "middle_school_biology",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于初中生物的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_middle_school_chemistry": {
+      "task": "ceval-valid_middle_school_chemistry",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "middle_school_chemistry",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于初中化学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_middle_school_geography": {
+      "task": "ceval-valid_middle_school_geography",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "middle_school_geography",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于初中地理的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_middle_school_history": {
+      "task": "ceval-valid_middle_school_history",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "middle_school_history",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于初中历史的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_middle_school_mathematics": {
+      "task": "ceval-valid_middle_school_mathematics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "middle_school_mathematics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于初中数学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_middle_school_physics": {
+      "task": "ceval-valid_middle_school_physics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "middle_school_physics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于初中物理的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_middle_school_politics": {
+      "task": "ceval-valid_middle_school_politics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "middle_school_politics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于初中政治的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_modern_chinese_history": {
+      "task": "ceval-valid_modern_chinese_history",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "modern_chinese_history",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于近代史纲要的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_operating_system": {
+      "task": "ceval-valid_operating_system",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "operating_system",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于操作系统的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_physician": {
+      "task": "ceval-valid_physician",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "physician",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于医师资格的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_plant_protection": {
+      "task": "ceval-valid_plant_protection",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "plant_protection",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于植物保护的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_probability_and_statistics": {
+      "task": "ceval-valid_probability_and_statistics",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "probability_and_statistics",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于概率统计的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_professional_tour_guide": {
+      "task": "ceval-valid_professional_tour_guide",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "professional_tour_guide",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于导游资格的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_sports_science": {
+      "task": "ceval-valid_sports_science",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "sports_science",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于体育学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_tax_accountant": {
+      "task": "ceval-valid_tax_accountant",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "tax_accountant",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于税务师的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ceval-valid_teacher_qualification": {
+      "task": "ceval-valid_teacher_qualification",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "teacher_qualification",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于教师资格的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
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+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
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+          "aggregation": "mean",
+          "higher_is_better": true
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+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
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+      "task": "ceval-valid_urban_and_rural_planner",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "urban_and_rural_planner",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于注册城乡规划师的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
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+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
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+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
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+    },
+    "ceval-valid_veterinary_medicine": {
+      "task": "ceval-valid_veterinary_medicine",
+      "group": "ceval-valid",
+      "dataset_path": "ceval/ceval-exam",
+      "dataset_name": "veterinary_medicine",
+      "validation_split": "val",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "以下是中国关于兽医学的单项选择题，请选出其中的正确答案。\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
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+          "higher_is_better": true
+        },
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+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
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+      "output_type": "multiple_choice",
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+      "metadata": {
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+      "likelihood_diff_stderr,none": 0.18292499522630842,
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+      "alias": " - crows_pairs_french"
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+      "alias": " - crows_pairs_french_age"
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+      "likelihood_diff_stderr,none": 0.4446184067415701,
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+      "pct_stereotype_stderr,none": 0.10415433852097383,
+      "alias": " - crows_pairs_french_autre"
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+      "likelihood_diff_stderr,none": 0.4061243122276867,
+      "pct_stereotype,none": 0.7121212121212122,
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+      "alias": " - crows_pairs_french_disability"
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+      "pct_stereotype_stderr,none": 0.027000334456667868,
+      "alias": " - crows_pairs_french_gender"
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+      "pct_stereotype_stderr,none": 0.031289438964526774,
+      "alias": " - crows_pairs_french_nationality"
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+      "pct_stereotype_stderr,none": 0.05533504751887218,
+      "alias": " - crows_pairs_french_physical_appearance"
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+      "pct_stereotype_stderr,none": 0.02316441640598207,
+      "alias": " - crows_pairs_french_race_color"
+    },
+    "crows_pairs_french_religion": {
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+      "likelihood_diff_stderr,none": 0.23687590039198891,
+      "pct_stereotype,none": 0.7478260869565218,
+      "pct_stereotype_stderr,none": 0.04067222754154718,
+      "alias": " - crows_pairs_french_religion"
+    },
+    "crows_pairs_french_sexual_orientation": {
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+      "likelihood_diff_stderr,none": 0.2861957499435561,
+      "pct_stereotype,none": 0.7912087912087912,
+      "pct_stereotype_stderr,none": 0.042843052065094325,
+      "alias": " - crows_pairs_french_sexual_orientation"
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+    "crows_pairs_french_socioeconomic": {
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+      "likelihood_diff_stderr,none": 0.2542396166949862,
+      "pct_stereotype,none": 0.6836734693877551,
+      "pct_stereotype_stderr,none": 0.03330234893102004,
+      "alias": " - crows_pairs_french_socioeconomic"
+    }
+  },
+  "groups": {
+    "crows_pairs": {
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+      "pct_stereotype,none": 0.6106141920095408,
+      "pct_stereotype_stderr,none": 0.06140619073838575,
+      "alias": "crows_pairs"
+    }
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+      "task": "crows_pairs_english",
+      "group": [
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+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
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+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
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+      "group": [
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+        "social_bias",
+        "loglikelihood"
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+      "fewshot_delimiter": "\n\n",
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+      "group": [
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+        "social_bias",
+        "loglikelihood"
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+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "english",
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+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
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+        "loglikelihood"
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+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
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+      "group": [
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+        "loglikelihood"
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+        "social_bias",
+        "loglikelihood"
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+      "group": [
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+        "social_bias",
+        "loglikelihood"
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+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_english_race_color": {
+      "task": "crows_pairs_english_race_color",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "english",
+      "test_split": "test",
+      "process_docs": "def filter_race_color(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"race-color\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_english_religion": {
+      "task": "crows_pairs_english_religion",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "english",
+      "test_split": "test",
+      "process_docs": "def filter_religion(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"religion\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_english_sexual_orientation": {
+      "task": "crows_pairs_english_sexual_orientation",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "english",
+      "test_split": "test",
+      "process_docs": "def filter_orientation(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"sexual-orientation\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_english_socioeconomic": {
+      "task": "crows_pairs_english_socioeconomic",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "english",
+      "test_split": "test",
+      "process_docs": "def filter_socio(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"socioeconomic\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french": {
+      "task": "crows_pairs_french",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_age": {
+      "task": "crows_pairs_french_age",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_age(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"age\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_autre": {
+      "task": "crows_pairs_french_autre",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_autre(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"autre\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_disability": {
+      "task": "crows_pairs_french_disability",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_disability(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"disability\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_gender": {
+      "task": "crows_pairs_french_gender",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_gender(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"gender\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_nationality": {
+      "task": "crows_pairs_french_nationality",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_nationality(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"nationality\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_physical_appearance": {
+      "task": "crows_pairs_french_physical_appearance",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_appearance(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"physical-appearance\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_race_color": {
+      "task": "crows_pairs_french_race_color",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_race_color(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"race-color\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_religion": {
+      "task": "crows_pairs_french_religion",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_religion(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"religion\")\n",
+      "doc_to_text": "",
+      "doc_to_target": 0,
+      "doc_to_choice": "def doc_to_choice(doc):\n    return [doc[\"sent_more\"], doc[\"sent_less\"]]\n",
+      "process_results": "def process_results(doc, results):\n    lls, _ = zip(*results)\n\n    likelihood1, likelihood2 = lls\n\n    # Calculate the absolute difference in loglikelihoods\n    diff = abs(likelihood1 - likelihood2)\n\n    # if stereotypical sentence more likely (loglikelihood higher)\n    # then treat this as predicting stereotyped sentence\n    acc = 1.0 if likelihood1 > likelihood2 else 0.0\n\n    return {\"likelihood_diff\": diff, \"pct_stereotype\": acc}\n",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "likelihood_diff",
+          "aggregation": "mean",
+          "higher_is_better": false
+        },
+        {
+          "metric": "pct_stereotype",
+          "aggregation": "mean",
+          "higher_is_better": false
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "crows_pairs_french_sexual_orientation": {
+      "task": "crows_pairs_french_sexual_orientation",
+      "group": [
+        "crows_pairs",
+        "social_bias",
+        "loglikelihood"
+      ],
+      "dataset_path": "BigScienceBiasEval/crows_pairs_multilingual",
+      "dataset_name": "french",
+      "test_split": "test",
+      "process_docs": "def filter_orientation(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"sexual-orientation\")\n",
+      "doc_to_text": "",
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+      "group": "kmmlu",
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+      "group": "kmmlu",
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+      "group": "kmmlu",
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+      "group": "kmmlu",
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+      "group": "kmmlu",
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+      "group": "kmmlu",
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+      "group": "kmmlu",
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+      "group": "kmmlu",
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+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
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+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
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+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.1
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+    "kmmlu_real_estate": {
+      "task": "kmmlu_real_estate",
+      "group": "kmmlu",
+      "dataset_path": "HAERAE-HUB/K-MMLU-Preview",
+      "dataset_name": "Real-Estate",
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+      "test_split": "test",
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+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'][answer-1]}}",
+      "doc_to_choice": [
+        "A",
+        "B",
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+      "target_delimiter": " ",
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+        {
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+      ],
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+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.1
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+    "kmmlu_refrigerating_machinery": {
+      "task": "kmmlu_refrigerating_machinery",
+      "group": "kmmlu",
+      "dataset_path": "HAERAE-HUB/K-MMLU-Preview",
+      "dataset_name": "Refrigerating-Machinery",
+      "training_split": "train",
+      "validation_split": "dev",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'][answer-1]}}",
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+        "A",
+        "B",
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+      "target_delimiter": " ",
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+      "should_decontaminate": false,
+      "metadata": {
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+    "kmmlu_social_welfare": {
+      "task": "kmmlu_social_welfare",
+      "group": "kmmlu",
+      "dataset_path": "HAERAE-HUB/K-MMLU-Preview",
+      "dataset_name": "Social-Welfare",
+      "training_split": "train",
+      "validation_split": "dev",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'][answer-1]}}",
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+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
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+    "kmmlu_taxation": {
+      "task": "kmmlu_taxation",
+      "group": "kmmlu",
+      "dataset_path": "HAERAE-HUB/K-MMLU-Preview",
+      "dataset_name": "Taxation",
+      "training_split": "train",
+      "validation_split": "dev",
+      "test_split": "test",
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+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답：",
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+      "metadata": {
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+      "task": "kmmlu_telecommunications_and_wireless_technology",
+      "group": "kmmlu",
+      "dataset_path": "HAERAE-HUB/K-MMLU-Preview",
+      "dataset_name": "Telecommunications-and-Wireless-Technology",
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+      "doc_to_text": "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답：",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'][answer-1]}}",
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+    "kmmlu_social_welfare": 0,
+    "kmmlu_taxation": 0,
+    "kmmlu_telecommunications_and_wireless_technology": 0
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+  "config": {
+    "model": "hf",
+    "model_args": "pretrained=bigscience/bloom-7b1,dtype=bfloat16,trust_remote_code=True",
+    "batch_size": "auto",
+    "batch_sizes": [
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+    "bootstrap_iters": 100000,
+    "gen_kwargs": null
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+  "git_hash": "62513ca"
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+  "results": {
+    "kobest": {
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+      "acc_stderr,none": 0.039805756990886364,
+      "f1,none": 0.4021326241581926,
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+      "alias": "kobest"
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+      "acc_stderr,none": 0.013347413060911655,
+      "f1,none": 0.4443203967898094,
+      "f1_stderr,none": "N/A",
+      "alias": " - kobest_boolq"
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+    "kobest_copa": {
+      "acc,none": 0.493,
+      "acc_stderr,none": 0.015817749561843578,
+      "f1,none": 0.4923781632499812,
+      "f1_stderr,none": "N/A",
+      "alias": " - kobest_copa"
+    },
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+      "acc_stderr,none": 0.021175665695209407,
+      "f1,none": 0.33643809850112993,
+      "f1_stderr,none": "N/A",
+      "acc_norm,none": 0.444,
+      "acc_norm_stderr,none": 0.022242244375731027,
+      "alias": " - kobest_hellaswag"
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+    "kobest_sentineg": {
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+      "acc_stderr,none": 0.02504922719608602,
+      "f1,none": 0.3381683908762308,
+      "f1_stderr,none": "N/A",
+      "alias": " - kobest_sentineg"
+    },
+    "kobest_wic": {
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+      "acc_stderr,none": 0.014088017407699532,
+      "f1,none": 0.3297230142969956,
+      "f1_stderr,none": "N/A",
+      "alias": " - kobest_wic"
+    }
+  },
+  "groups": {
+    "kobest": {
+      "acc,none": 0.4764306073229555,
+      "acc_stderr,none": 0.039805756990886364,
+      "f1,none": 0.4021326241581926,
+      "f1_stderr,none": "N/A",
+      "acc_norm,none": 0.444,
+      "acc_norm_stderr,none": 0.0004947174348697385,
+      "alias": "kobest"
+    }
+  },
+  "configs": {
+    "kobest_boolq": {
+      "task": "kobest_boolq",
+      "group": [
+        "kobest"
+      ],
+      "dataset_path": "skt/kobest_v1",
+      "dataset_name": "boolq",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "{{paragraph}} 질문: {{question}} 답변: ",
+      "doc_to_target": "{{label}}",
+      "doc_to_choice": [
+        "아니오",
+        "예"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "f1",
+          "aggregation": "def macro_f1_score(items):\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average='macro')\n    return fscore\n",
+          "average": "macro",
+          "hf_evaluate": true,
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "kobest_copa": {
+      "task": "kobest_copa",
+      "group": [
+        "kobest"
+      ],
+      "dataset_path": "skt/kobest_v1",
+      "dataset_name": "copa",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def copa_doc_to_text(doc: dict) -> str:\n    connector = {\"원인\": \" 왜냐하면\", \"결과\": \" 그래서\"}[doc[\"question\"].strip()]\n    return f\"\"\"{doc[\"premise\"]} {connector}\"\"\"\n",
+      "doc_to_target": "def copa_doc_to_target(doc: dict) -> str:\n    correct_choice = doc[\"alternative_1\"] if doc[\"label\"] == 0 else doc[\"alternative_2\"]\n    return f\"\"\"{correct_choice}\"\"\"\n",
+      "doc_to_choice": "def copa_doc_to_choice(doc: dict) -> list:\n    return [f\"\"\"{doc[\"alternative_1\"]}\"\"\", f\"\"\"{doc[\"alternative_2\"]}\"\"\"]\n",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "f1",
+          "aggregation": "def macro_f1_score(items):\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average='macro')\n    return fscore\n",
+          "average": "macro",
+          "hf_evaluate": true,
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "kobest_hellaswag": {
+      "task": "kobest_hellaswag",
+      "group": [
+        "kobest"
+      ],
+      "dataset_path": "skt/kobest_v1",
+      "dataset_name": "hellaswag",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "process_docs": "def hellaswag_process_doc(doc: Dataset) -> Dataset:\n    def preprocessor(dataset):\n        return {\n            \"query\": f\"\"\"문장: {dataset[\"context\"]}\"\"\",\n            \"choices\": [dataset[\"ending_1\"], dataset[\"ending_2\"], dataset[\"ending_3\"], dataset[\"ending_4\"]],\n            \"gold\": int(dataset[\"label\"]),\n        }\n\n    return doc.map(preprocessor)\n",
+      "doc_to_text": "{{query}}",
+      "doc_to_target": "{{label}}",
+      "doc_to_choice": "choices",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "f1",
+          "aggregation": "def macro_f1_score(items):\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average='macro')\n    return fscore\n",
+          "average": "macro",
+          "hf_evaluate": true,
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "kobest_sentineg": {
+      "task": "kobest_sentineg",
+      "group": [
+        "kobest"
+      ],
+      "dataset_path": "skt/kobest_v1",
+      "dataset_name": "sentineg",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def sentineg_doc_to_text(doc: dict):\n    return f\"\"\"문장: {doc[\"sentence\"]} 긍부정:\"\"\"\n",
+      "doc_to_target": "{{label}}",
+      "doc_to_choice": [
+        "부정",
+        "긍정"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "f1",
+          "aggregation": "def macro_f1_score(items):\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average='macro')\n    return fscore\n",
+          "average": "macro",
+          "hf_evaluate": true,
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "kobest_wic": {
+      "task": "kobest_wic",
+      "group": [
+        "kobest"
+      ],
+      "dataset_path": "skt/kobest_v1",
+      "dataset_name": "wic",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def wic_doc_to_text(doc: dict) -> str:\n    return f\"\"\"문장1: {doc[\"context_1\"]} 문장2: {doc[\"context_2\"]} 두 문장에서 {doc[\"word\"]}가 같은 뜻으로 쓰였나?\"\"\"\n",
+      "doc_to_target": "{{label}}",
+      "doc_to_choice": [
+        "아니오",
+        "예"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
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