@inproceedings{wu-etal-2025-combining,
    title = "Combining the Best of Both Worlds: A Method for Hybrid {NMT} and {LLM} Translation",
    author = "Wu, Zhanglin  and
      Wei, Daimeng  and
      Chen, Xiaoyu  and
      Shang, Hengchao  and
      Guo, Jiaxin  and
      Li, Zongyao  and
      Luo, Yuanchang  and
      Yang, Jinlong  and
      Rao, Zhiqiang  and
      Yang, Hao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.266/",
    pages = "5140--5148",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language model (LLM) shows promising performances in a variety of downstream tasks, such as machine translation (MT). However, using LLMs for translation suffers from high computational costs and significant latency. Based on our evaluation, in most cases, translations using LLMs are comparable to that generated by neural machine translation (NMT) systems. Only in particular scenarios, LLM and NMT models show respective advantages. As a result, integrating NMT and LLM for translation and using LLM only when necessary seems to be a sound solution. A scheduling policy that optimizes translation result while ensuring fast speed and as less LLM usage as possible is thereby required. We compare several scheduling policies and propose a novel and straightforward decider that leverages source sentence features. We conduct extensive experiments on multilingual test sets and the result shows that we can achieve optimal translation performance with less LLM usage, demonstrating effectiveness of our decider."
}