mlsanityData to 7 Oct 2026

C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

arXiv 2305.08322 ↗First posted 15 May 2023cs.CLcited by 694

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Cited by 77 in Mixture-of-experts, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

New NLP benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context. C-Eval comprises multiple-choice questions across four difficulty levels: middle school, high school, college, and professional. The questions span 52 diverse disciplines, ranging from humanities to science and engineering. C-Eval is accompanied by C-Eval Hard, a subset of very challenging subjects in C-Eval that requires advanced reasoning abilities to solve. We conduct a comprehensive evaluation of the most advanced LLMs on C-Eval, including both English- and Chinese-oriented models. Results indicate that only GPT-4 could achieve an average accuracy of over 60%, suggesting that there is still significant room for improvement for current LLMs. We anticipate C-Eval will help analyze important strengths and shortcomings of foundation models, and foster their development and growth for Chinese users.

Key facts. Cited by 694 papers in the map (data through 7 Oct 2026). Builds on 35 of its 48 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

35 of 48 references.

References · 35

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
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  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
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  4. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  5. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  6. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  7. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  8. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,767
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  10. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
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  13. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  14. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  15. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  16. 2022Galactica: A Large Language Model for Sciencecited by 736
  17. 2023AGIEval: A Human-Centric Benchmark for Evaluating Foundation Modelscited by 701
  18. 2022PAL: Program-aided Language Modelscited by 635
  19. 2023Augmented Language Models: a Surveycited by 398
  20. 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
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Citing papers per year: 2023: 110; 2024: 235; 2025: 200; 2026: 149.

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50 most cited of 694 All 694 (JSON)

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