https://github.com/TIGER-AI-Lab/MMLU-Pro

The code and data for "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark" [NeurIPS 2024]
https://github.com/TIGER-AI-Lab/MMLU-Pro

evaluation llm

Last synced: 5 months ago
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The code and data for "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark" [NeurIPS 2024]

• Host: GitHub
• URL: https://github.com/TIGER-AI-Lab/MMLU-Pro
• Owner: TIGER-AI-Lab
• License: apache-2.0
• Created: 2024-05-16T23:59:51.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-10-24T03:56:29.000Z (6 months ago)
• Last Synced: 2024-10-25T06:42:07.270Z (6 months ago)
• Topics: evaluation, llm
• Language: Python
• Homepage:
• Size: 317 MB
• Stars: 117
• Watchers: 2
• Forks: 21
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-golang-ai - MMLU-Pro - Task Language Understanding Benchmark. (Benchmark / English)
• awesome-llm-eval - MMLU-Pro - Pro 是 MMLU 数据集的改进版本。MMLU 一直是多选知识数据集的参考。然而，最近的研究表明它既包含噪音（一些问题无法回答），又太容易（通过模型能力的进化和污染的增加）。MMLU-Pro 向模型提供十个选择而不是四个，要求在更多问题上进行推理，并经过专家审查以减少噪音量。它比原版质量更高且更难. MMLU-Pro减少了提示变化对模型性能的影响，这是其前身MMLU常见的问题。研究发现，在这个新基准上使用“Chain of Thought”推理的模型表现更好，这表明MMLU-Pro更适合评估人工智能的微妙推理能力. (2024-05-20) | (Datasets-or-Benchmark / 通用)