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home / papers / 2023 / M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models

M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models

Paper · 2023 · Chuang Liu, Renren Jin, Yuqi Ren et al.

Summary

Chinese large language models (LLMs) are improving quickly, but there has been no large Chinese benchmark that tests their knowledge the way the Chinese education system does. Translating English benchmarks such as MMLU leaves gaps, because curricula and knowledge structures differ between countries. M3KE addresses this gap. The authors gathered four-option multiple-choice questions from public websites, preferring entrance-exam material wherever possible. The questions span every stage from primary school to college, plus professional and general exams. Subjects range from law, medicine and psychology to arts, religion and classical Chinese.

The authors tested open-source Chinese LLMs from 335M to 130B parameters, using unified zero- and few-shot prompts, with GPT-3.5-turbo as a reference. Most open models scored close to chance, even on primary-school tasks. Every model, including GPT-3.5-turbo, stayed below roughly 50% accuracy. The benchmark is therefore demanding and well suited to tracking how Chinese LLMs progress in acquiring and applying knowledge.

Key points

  • Scale and structure: M3KE contains 20,477 questions across 71 tasks.
    • Each task provides a 5-question few-shot set and a test portion of at least 100 items.
    • The test portion totals 20,122 questions.
    • By task count, M3KE exceeds MMLU (57), MMCU (51) and AGIEval (20).
  • Models evaluated:
    • Pre-trained: GLM-335M, BLOOM-7.1B, GLM-10B and GLM-130B.
    • SFT variants: ChatGLM-6B, MOSS-SFT-16B, BELLE-7B-0.2M and BELLE-7B-2M.
  • Headline results:
    • GPT-3.5-turbo reaches about 48% accuracy.
    • The best open model, BELLE-7B-2M (built on BLOOMZ-7.1B-MT), is 14.8 points behind it.
    • In the zero-shot setting, pre-trained models with 10B parameters or fewer fall below the 25% random baseline.
  • Factors behind performance:
    • Among pre-trained models, accuracy grows with model size.
    • More instruction data helps: the BELLE model tuned on 2M instructions outperforms the one tuned on 0.2M.
    • Five-shot prompting mostly helps small pre-trained models but hurts GLM-130B, ChatGLM-6B and BELLE-7B-2M.
  • Education levels: Models do not perform better on lower education levels than on higher ones, so primary-school tasks remain hard even for GPT-3.5-turbo.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢Huawei TechnologiesShenzhen · China75 works · 12,975 citations🎓Tianjin UniversityTianjin · China41 works · 1,128 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksEvaluation & benchmarks · 1,220 works

Cite

Cite as Chuang Liu, Renren Jin, Yuqi Ren, Linhao Yu, Tianyu Dong, Xiaohan Peng, Shuting Zhang, Jianxiang Peng, Peiyi Zhang, Qingqing Lyu, Xiaowen Su, Qun Liu and Deyi Xiong. 2023. M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models. arXiv:2305.10263.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2305.10263 · arXiv 2305.10263

@misc{liu2023m3ke,
  title = {{M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models}},
  author = {Chuang Liu, Renren Jin, Yuqi Ren, Linhao Yu, Tianyu Dong, Xiaohan Peng, Shuting Zhang, Jianxiang Peng, Peiyi Zhang, Qingqing Lyu, Xiaowen Su, Qun Liu and Deyi Xiong},
  year = {2023},
  doi = {10.48550/arxiv.2305.10263},
  eprint = {2305.10263},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.10263}
}

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