@inproceedings{vilar-etal-2023-prompting,
    title = "Prompting {P}a{LM} for Translation: Assessing Strategies and Performance",
    author = "Vilar, David  and
      Freitag, Markus  and
      Cherry, Colin  and
      Luo, Jiaming  and
      Ratnakar, Viresh  and
      Foster, George",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.859",
    doi = "10.18653/v1/2023.acl-long.859",
    pages = "15406--15427",
    abstract = "Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages. We probe this ability in an in-depth study of the pathways language model (PaLM), which has demonstrated the strongest machine translation (MT) performance among similarly-trained LLMs to date. We investigate various strategies for choosing translation examples for few-shot prompting, concluding that example quality is the most important factor. Using optimized prompts, we revisit previous assessments of PaLM{'}s MT capabilities with more recent test sets, modern MT metrics, and human evaluation, and find that its performance, while impressive, still lags that of state-of-the-art supervised systems. We conclude by providing an analysis of PaLM{'}s MT output which reveals some interesting properties and prospects for future work.",
}

Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages. We probe this ability in an in-depth study of the pathways language model (PaLM), which has demonstrated the strongest machine translation (MT) performance among similarly-trained LLMs to date. We investigate various strategies for choosing translation examples for few-shot prompting, concluding that example quality is the most important factor. Using optimized prompts, we revisit previous assessments of PaLM{'}s MT capabilities with more recent test sets, modern MT metrics, and human evaluation, and find that its performance, while impressive, still lags that of state-of-the-art supervised systems. We conclude by providing an analysis of PaLM{'}s MT output which reveals some interesting properties and prospects for future work.
Vilar, David Freitag, Markus Cherry, Colin Luo, Jiaming Ratnakar, Viresh Foster, George
Prompting PaLM for Translation: Assessing Strategies and Performance