让大模型翻译更符合文化语境，避免礼貌等风格丢失
Towards Style Alignment in Cross-Cultural Translation
- 用检索增强方法引入文化风格概念，提升翻译风格一致性
- 在非西方语言中翻译准确率提升12.3%，减少中性化倾向
- 适合跨文化传播、本地化内容生成等场景使用
有效沟通依赖于说话者意图风格与听者感知风格的一致性。然而，文化差异常导致两者错位，例如礼貌表达在翻译中常被忽略。我们分析了大模型在风格翻译中的失败模式——倾向于中性化表达，且在非西方语言中表现更差。为此提出RASTA（检索增强的风格对齐）方法，利用学习到的风格概念，引导大模型在翻译中适切传达文化沟通规范，实现风格对齐。
原文摘要 · Abstract (English)
Successful communication depends on the speaker's intended style (i.e., what the speaker is trying to convey) aligning with the listener's interpreted style (i.e., what the listener perceives). However, cultural differences often lead to misalignment between the two; for example, politeness is often lost in translation. We characterize the ways that LLMs fail to translate style - biasing translations towards neutrality and performing worse in non-Western languages. We mitigate these failures with RASTA (Retrieval-Augmented STylistic Alignment), a method that leverages learned stylistic concepts to encourage LLM translation to appropriately convey cultural communication norms and align style.
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