@inproceedings{fischer-volk-2025-name,
    title = "Name Consistency in {LLM}-based Machine Translation of Historical Texts",
    author = "Fischer, Dominic P.  and
      Volk, Martin",
    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-1.16/",
    pages = "204--219",
    ISBN = "978-2-9701897-0-1",
    abstract = "Large Language Models (LLMs) excel at translating 16th-century letters from Latin and Early New High German to modern English and German. While they perform well at translating well-known historical city names (e.g., Lutetia {--}{\ensuremath{>}} Paris), their ability to handle person names (e.g., Theodor Bibliander) or lesser-known toponyms (e.g., Augusta Vindelicorum {--}{\ensuremath{>}} Augsburg) remains unclear. This study investigates LLM-based translations of person and place names across various frequency bands in a corpus of 16th-century letters. Our results show that LLMs struggle with person names, achieving accuracies around 60{\%}, but perform better with place names, reaching accuracies around 90{\%}. We further demonstrate that including a translation suggestion for the proper noun in the prompt substantially boosts accuracy, yielding highly reliable results."
}