open-llm-leaderboard-old/details_eren23__FrankenBeagle-SmallOverlap-test

该数据集是在模型 eren23/FrankenBeagle-SmallOverlap-test 在 Open LLM Leaderboard 上的评估运行期间自动创建的。数据集由 63 个配置组成，每个配置对应一个评估任务。数据集是从 1 次运行中创建的，每次运行在每个配置中表示为特定的拆分，使用运行的时间戳命名。train 拆分始终指向最新的结果。一个额外的配置 results 存储了运行的所有聚合结果，用于计算和显示 Open LLM Leaderboard 上的聚合指标。README 还提供了如何使用 Python 中的 datasets 库加载运行细节的示例。

原始信息汇总

数据集概述

该数据集是在评估模型 eren23/FrankenBeagle-SmallOverlap-test 在 Open LLM Leaderboard 上的运行过程中自动创建的。数据集包含 63 个配置，每个配置对应一个评估任务。

数据集结构

• 配置数量：63 个配置
• 数据来源：从 1 次运行中创建，每个运行可以在每个配置中找到特定的分割，分割名称使用运行的时间戳。
• 训练分割："train" 分割始终指向最新的结果。
• 结果配置："results" 配置存储所有运行的聚合结果，用于计算和显示在 Open LLM Leaderboard 上的聚合指标。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_eren23__FrankenBeagle-SmallOverlap-test", "harness_winogrande_5", split="train")

最新结果

以下是 最新结果 的摘要：

python { "all": { "acc": 0.6516048577093372, "acc_stderr": 0.03217886824872047, "acc_norm": 0.6522986567968078, "acc_norm_stderr": 0.03283383614658343, "mc1": 0.5642594859241126, "mc1_stderr": 0.017358345398863134, "mc2": 0.6969160518300113, "mc2_stderr": 0.015146787132780715 }, "harness|arc:challenge|25": { "acc": 0.6945392491467577, "acc_stderr": 0.013460080478002505, "acc_norm": 0.7201365187713311, "acc_norm_stderr": 0.01311904089772592 }, "harness|hellaswag|10": { "acc": 0.7171878111929895, "acc_stderr": 0.004494454911844622, "acc_norm": 0.8815972913762199, "acc_norm_stderr": 0.003224240722351316 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.02815283794249386, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.02815283794249386 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878151, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878151 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.025305906241590632, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.025305906241590632 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479048, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479048 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6820512820512821, "acc_stderr": 0.023610884308927865, "acc_norm": 0.6820512820512821, "acc_norm_stderr": 0.023610884308927865 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524572, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524572 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6596638655462185, "acc_stderr": 0.030778057422931673, "acc_norm": 0.6596638655462185, "acc_norm_stderr": 0.030778057422931673 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8366972477064221, "acc_stderr": 0.01584825580650155, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.01584825580650155 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5555555555555556, "acc_stderr": 0.03388857118502325, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.03388857118502325 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6995515695067265, "acc_stderr": 0.030769352008229143, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.030769352008229143 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7786259541984732, "acc_stderr": 0.03641297081313729, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313729 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165612, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165612 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7254335260115607, "acc_stderr": 0.02402774515526502, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.02402774515526502 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.42793296089385474, "acc_stderr": 0.01654788799741611, "acc_norm": 0.42793296089385474, "acc_norm_stderr": 0.01654788799741611 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818737, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818737 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.025583062489984813, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.025583062489984813 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47131681877444587, "acc_stderr": 0.012749206007657476, "acc_norm": 0.47131681877444587, "acc_norm_stderr": 0.012749206007657476 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462927, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462927 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6699346405228758, "acc_stderr": 0.019023726160724553, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.019023726160724553 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7224489795918367, "acc_stderr": 0.02866685779027465, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.02866685779027465 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578323, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578323 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.03379976689896308, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896308 }, "harness|hendrycksTest-virology|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness|truthfulqa:mc|0": { "mc1": 0.5642594859241126, "mc1_stderr": 0.017358345398863134, "mc2": 0.6969160518300113, "mc2_stderr": 0.015146787132780715 }, "harness|winogrande|5": { "acc": 0.8184688239936859, "acc_stderr": 0.01083327651500748 }, "harness|gsm8k|5": { "acc": 0.6338134950720242, "acc_stderr": 0.013270100238748835 } }

数据集配置

• 配置名称：harness_arc_challenge_25

  • 数据文件：
    • 分割：2024_01_28T18_01_48.091573
      • 路径：**/details_harness|arc:challenge|25_2024-01-28T18-01-48.091573.parquet
    • 分割：latest
      • 路径：**/details_harness|arc:challenge|25_2024-01-28T18-01-48.091573.parquet

• 配置名称：harness_gsm8k_5

  • 数据文件：
    • 分割：2024_01_28T18_01_48.091573
      • 路径：**/details_harness|gsm8k|5_2024-01-28T18-01-48.091573.parquet
    • 分割：latest
      • 路径：**/details_harness|gsm8k|5_2024-01-28T18-01-48.091573.parquet

• 配置名称：harness_hellaswag_10

  • 数据文件：
    • 分割：2024_01_28T18_01_48.091573
      • 路径：**/details_harness|hellaswag|10_2024-01-28T18-01-48.091573.parquet
    • 分割：latest
      • 路径：**/details_harness|hellaswag|10_2024-01-28T18-01-48.091573.parquet

• 配置名称：harness_hendrycksTest_5

  • 数据文件：
    • 分割：2024_01_28T18_01_48.091573
      • 路径：
        • **/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-anatomy|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-astronomy|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-business_ethics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-college_biology|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-college_medicine|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-college_physics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-computer_security|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-econometrics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-formal_logic|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-global_facts|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-human_aging|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-international_law|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-machine_learning|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-management|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-marketing|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-nutrition|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-philosophy|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-prehistory|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-professional_law|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-public_relations|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-security_studies|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-sociology|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-virology|5_2024-01-28T18-01-48.091573.parquet
        • **/details_harness|hendrycksTest-world_religions|5_2024-01-28T18-01-48.091573.parquet

搜集汇总

数据集介绍

构建方式

在开放大语言模型排行榜（Open LLM Leaderboard）的评估框架下，该数据集为模型eren23/FrankenBeagle-SmallOverlap-test的自动化评测产物。数据集由63个配置组成，每个配置对应一个被评估的任务，覆盖了从常识推理到多学科知识等广泛领域。数据来源于单次运行，每次运行的时间戳被用作数据分割的标识，其中“train”分割始终指向最新一次运行的结果。此外，一个名为“results”的额外配置存储了所有运行的聚合结果，用于在排行榜上计算和展示综合指标。

特点

该数据集具有结构化与时效性并重的特点。其63个配置精准映射了不同的评估任务，如ARC挑战赛、HellaSwag、GSM8K以及涵盖57个学科的MMLU测试，确保了评估维度的全面性。通过时间戳分割机制，数据集能够清晰追踪模型性能的演变轨迹，而“train”分割的动态更新则保证了用户始终能获取到最新的评估细节。这种设计不仅便于进行历史对比分析，也提升了数据检索的直观性。

使用方法

用户可通过Hugging Face的datasets库便捷地加载该数据集。例如，使用load_dataset函数，指定数据集名称和具体的任务配置（如“harness_winogrande_5”），并选择“train”分割即可获取最新运行结果。加载后的数据以Parquet格式存储，支持高效的数据处理与查询。对于需要深入分析的任务，用户可依据配置文件中的路径索引到对应的Parquet文件，从而进行细粒度的评估数据挖掘与模型性能研究。

背景与挑战

背景概述

该数据集源自HuggingFace社区发起的开放大语言模型排行榜（Open LLM Leaderboard），由HuggingFace团队于2023年创建，旨在系统性地评估和比较各类开源大语言模型在多样化任务上的表现。核心研究问题在于如何客观、可复现地衡量模型在推理、常识、数学及多领域知识等维度的能力。数据集记录了模型FrankenBeagle-SmallOverlap-test在63个配置下的详细评测结果，涵盖ARC-Challenge、HellaSwag、MMLU、TruthfulQA、Winogrande及GSM8K等基准测试，为社区提供了透明且标准化的性能对比依据。该数据集的出现推动了开源模型评测的规范化进程，使研究者能够更清晰地洞察模型优势与局限，对促进大语言模型的迭代优化具有重要影响力。

当前挑战

该数据集所面对的领域挑战在于大语言模型评测的全面性与公平性。一方面，现有基准测试难以覆盖真实世界中复杂多变的语言理解与推理场景，模型在特定任务上的高分可能无法反映其泛化能力，例如在MMLU的抽象代数（acc仅0.33）与高中数学（acc约0.33）等科目上表现薄弱，揭示了领域知识的深度匮乏。另一方面，构建过程中面临评测标准统一化的难题，不同任务需设计适配的提示模板与评估指标，且需避免数据泄露导致的性能虚高。此外，数据集依赖单一时间点的评测快照，难以捕捉模型迭代后的动态变化，而评测结果的可复现性则受限于计算环境与随机种子等变量。

常用场景

经典使用场景

在自然语言处理与大规模语言模型评估领域，Open LLM Leaderboard的评估数据集已成为衡量模型综合性能的黄金标准。该数据集通过整合ARC、HellaSwag、MMLU、TruthfulQA、Winogrande和GSM8K等多项经典基准任务，为研究者提供了一个系统化的模型评测平台。其典型使用场景涵盖从常识推理、知识问答到数学求解的多维度能力检验，尤其适用于对比不同架构或训练策略下语言模型的泛化表现，是模型迭代与学术论文中不可或缺的评估工具。

解决学术问题

该数据集有效解决了大语言模型评估中存在的基准碎片化与结果不可复现问题。通过统一配置63个细分任务并标准化评测流程，它消除了因不同实现细节导致的指标偏差，使研究者能够公平比较模型在常识推理、多领域知识掌握、数学逻辑及对抗性问答等维度的真实能力。其意义在于推动了模型评估的透明化与规范化，为理解模型在知识边界、鲁棒性和推理深度上的局限性提供了量化依据，深刻影响了后续关于模型能力边界与安全性的研究范式。

衍生相关工作

该数据集衍生了一系列经典工作，包括基于其评测结果分析模型缩放定律的研究，以及探索不同训练数据组成对下游任务影响的消融实验。具体而言，研究者利用该数据集验证了指令微调、强化学习与模型蒸馏等技术对多任务泛化能力的提升效果，并催生了如Open LLM Leaderboard上的动态排名系统，成为社区公认的模型性能基准。此外，其细粒度的任务结果也被用于训练性能预测器，推动了自动化模型架构搜索与高效评测方法的发展。