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EMNLP Findings2024

AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models

Yuting Wei, Yuanxing Xu, Xinru Wei, Simin Yang, Yangfu Zhu, Yuqing Li, Di Liu, Bin Wu

Abstract

Given the importance of ancient Chinese in capturing the essence of rich historical and cultural heritage, the rapid advancements in Large Language Models (LLMs) necessitate benchmarks that can effectively evaluate their understanding of ancient contexts. To meet this need, we present AC-EVAL, an innovative benchmark designed to assess the advanced knowledge and reasoning capabilities of LLMs within the context of ancient Chinese. AC-EVAL is structured across three levels of difficulty reflecting different facets of language comprehension: general historical knowledge, short text understanding, and long text comprehension. The benchmark comprises 13 tasks, spanning historical facts, geography, social customs, art, philosophy, classical poetry and prose, providing a comprehensive assessment framework. Our extensive evaluation of top-performing LLMs, tailored for both English and Chinese, reveals a substantial potential for enhancing ancient text comprehension. By highlighting the strengths and weaknesses of LLMs, AC-EVAL aims to promote their development and application forward in the realms of ancient Chinese language education and scholarly research. 1

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Cited by top-tier papers3

  1. AncientBench: Towards Comprehensive Evaluation on Excavated and Transmitted Chinese CorporaZhihan Zhou, Daqian Shi, Rui Song, Lida Shi et al.AAAI 2026 · 1 citation
  2. Incorporating Diverse Perspectives in Cultural Alignment: Survey of Evaluation Benchmarks Through A Three-Dimensional FrameworkMeng-Chen Wu, Si-Chi Chin, Tess Wood, Ayush Goyal et al.EMNLP 2025
  3. InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language ModelingXiaolei Diao, Zhihan Zhou, Lida Shi, Ting Wang et al.AAAI 2026

Builds on8

  1. Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou et al.ICLR 2021 · 7,905 citations
  2. Least-to-Most Prompting Enables Complex Reasoning in Large Language ModelsDenny Zhou, Nathanael Schärli, Le Hou, Jason Wei et al.ICLR 2023 · 318 citations
  3. GLM-130B: An Open Bilingual Pre-trained ModelAohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang et al.ICLR 2023 · 295 citations
  4. Automatic Chain of Thought Prompting in Large Language ModelsZhuosheng Zhang, Aston Zhang, Mu Li, Alex SmolaICLR 2023 · 234 citations
  5. CMMLU: Measuring massive multitask language understanding in ChineseHaonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang et al.ACL Findings 2024 · 105 citations

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  2. Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge ReasoningHaiyang Yu, Yuchuan Wu, Fan Shi, Jinghui Lu et al.ACL Findings 2026 · 3 citations
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