Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation
Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorly understood. To bridge this gap, we introduce CanMT, a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation, together with a theoretically grounded, multi-dimensional evaluation framework for assessing cultural translation quality. Leveraging CanMT, we systematically evaluate a wide range of LLMs and translation systems under different translation strategy constraints. Our findings reveal substantial performance disparities across models and demonstrate that translation strategies exert a systematic influence on model behav...
| Authors | Zekun Yuan, Yangfan Ye, Xiaocheng Feng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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