@inproceedings{mu-etal-2023-augmenting,
    title = "Augmenting Large Language Model Translators via Translation Memories",
    author = "Mu, Yongyu  and
      Reheman, Abudurexiti  and
      Cao, Zhiquan  and
      Fan, Yuchun  and
      Li, Bei  and
      Li, Yinqiao  and
      Xiao, Tong  and
      Zhang, Chunliang  and
      Zhu, Jingbo",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.653",
    doi = "10.18653/v1/2023.findings-acl.653",
    pages = "10287--10299",
    abstract = "Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to {``}understand{''} prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.",
}

Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to {``}understand{''} prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.
Mu, Yongyu Reheman, Abudurexiti Cao, Zhiquan Fan, Yuchun Li, Bei Li, Yinqiao Xiao, Tong Zhang, Chunliang Zhu, Jingbo
Augmenting Large Language Model Translators via Translation Memories