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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

摘要

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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引用它的顶会 Paper3

  1. AncientBench: Towards Comprehensive Evaluation on Excavated and Transmitted Chinese CorporaZhihan Zhou, Daqian Shi, Rui Song, Lida Shi 等AAAI 2026 · 1 次引用
  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 等EMNLP 2025
  3. InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language ModelingXiaolei Diao, Zhihan Zhou, Lida Shi, Ting Wang 等AAAI 2026

它引用的 Paper8

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

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