Evaluating LLMs on Chinese Idiom Translation

Cai Yang, Yao Dou, David Heineman, Xiaofeng Wu, Wei Xu

Keywords: Large Language Model Evaluation, Chinese Idioms, Meta Analysis, Multilingual Evaluation

TL;DR: We evaluate large language models on Chinese idiom translation across multiple domains and introduce new metrics that reliably measures idiom translation quality, significantly outperforming existing translation metrics.

Abstract:

Idioms, whose figurative meanings usually differ from their literal interpretations, are common in everyday language, especially in Chinese, where they often contain historical references and follow specific structural patterns. Despite recent progress in machine translation with large language models, little is known about Chinese idiom translation. In this work, we introduce IdiomEval, a framework with a comprehensive error taxonomy for Chinese idiom translation. We annotate 900 translation pairs from nine modern systems, including GPT-4o and Google Translate, across four domains: web, news, Wikipedia, and social media. We find these systems fail at idiom translation, producing incorrect, literal, partial, or even missing translations. The best-performing system, GPT-4, makes errors in 28\% of cases.
We also find that existing evaluation metrics measure idiom quality poorly with Pearson correlation below 0.48 with human ratings. We thus develop improved models that achieve F$_1$ scores of 0.68 for detecting idiom translation errors.

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Submission Number: 992