@inproceedings{yang-etal-2024-direct,
    title = "Direct Preference Optimization for Neural Machine Translation with Minimum {B}ayes Risk Decoding",
    author = "Yang, Guangyu  and
      Chen, Jinghong  and
      Lin, Weizhe  and
      Byrne, Bill",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.naacl-short.34/",
    doi = "10.18653/v1/2024.naacl-short.34",
    pages = "391--398",
    abstract = "Minimum Bayes Risk (MBR) decoding can significantly improve translation performance of Multilingual Large Language Models (MLLMs). However, MBR decoding is computationally expensive. We show how the recently developed Reinforcement Learning technique, Direct Preference Optimization (DPO), can fine-tune MLLMs to get the gains of MBR without any additional computation in inference. Our method uses only a small monolingual fine-tuning set and yields significantly improved performance on multiple NMT test sets compared to MLLMs without DPO."
}