@inproceedings{azami-etal-2025-patent,
    title = "Patent Claim Translation via Continual Pre-training of Large Language Models with Parallel Data",
    author = "Azami, Haruto  and
      Kondo, Minato  and
      Utsuro, Takehito  and
      Nagata, Masaaki",
    editor = "Bouillon, Pierrette  and
      Gerlach, Johanna  and
      Girletti, Sabrina  and
      Volkart, Lise  and
      Rubino, Raphael  and
      Sennrich, Rico  and
      Farinha, Ana C.  and
      Gaido, Marco  and
      Daems, Joke  and
      Kenny, Dorothy  and
      Moniz, Helena  and
      Szoc, Sara",
    booktitle = "Proceedings of Machine Translation Summit XX: Volume 1",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.mtsummit-1.23/",
    pages = "300--314",
    ISBN = "978-2-9701897-0-1",
    abstract = "Recent advancements in large language models (LLMs) have enabled their application across various domains. However, in the field of patent translation, Transformer encoder-decoder based models remain the standard approach, and the potential of LLMs for translation tasks has not been thoroughly explored. In this study, we conducted patent claim translation using an LLM fine-tuned with parallel data through continual pre-training and supervised fine-tuning, following the methodology proposed by Guo et al. (2024) and Kondo et al. (2024). Comparative evaluation against the Transformer encoder-decoder based translations revealed that the LLM achieved high scores for both BLEU and COMET. This demonstrated improvements in addressing issues such as omissions and repetitions. Nonetheless, hallucination errors, which were not observed in the traditional models, occurred in some cases and negatively affected the translation quality. This study highlights the promise of LLMs for patent translation while identifying the challenges that warrant further investigation."
}