A Comparative Study on Large Language Models in Translating Classical Chinese Philosophical Terminology: A Benchmark Evaluation Against Official Renderings from Key Concepts in Chinese Thought and Culture: Philosophy
- DOI
- 10.2991/978-2-38476-551-5_55How to use a DOI?
- Keywords
- Large Language Models; ChatGPT; ERNIE Bot; Terminology Translation
- Abstract
The accurate translation of classical Chinese philosophical terms serves as a core vehicle for conveying ancient wisdom and a bridge facilitating dialogue between Eastern and Western civilizations, holding profound significance for safeguarding the diversity of global civilizations. Based on the English translations in Key Concepts in Chinese Thought and Culture: Philosophy, this paper selects typical translation cases of Chinese philosophical terms, conducts a comparative analysis by ChatGPT and ERNIE Bot against official translations, and explores the efficacy and limitations of Large Language Models (LLMs) in the English translation of Chinese philosophical terms, aiming to provide references for term translation practices in cross-cultural contexts.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Qin Nan AU - Xing Hao PY - 2026 DA - 2026/03/26 TI - A Comparative Study on Large Language Models in Translating Classical Chinese Philosophical Terminology: A Benchmark Evaluation Against Official Renderings from Key Concepts in Chinese Thought and Culture: Philosophy BT - Proceeding of 2025 8th International Conference on Humanities Education and Social Sciences (ICHESS 2025) PB - Atlantis Press SP - 498 EP - 509 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-551-5_55 DO - 10.2991/978-2-38476-551-5_55 ID - Nan2026 ER -