@inproceedings{havaldar-etal-2025-towards,
    title = "Towards Style Alignment in Cross-Cultural Translation",
    author = "Havaldar, Shreya  and
      Stein, Adam  and
      Wong, Eric  and
      Ungar, Lyle",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.acl-long.1550/",
    doi = "10.18653/v1/2025.acl-long.1550",
    pages = "32213--32230",
    ISBN = "979-8-89176-251-0",
    abstract = "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."
}