@inproceedings{hagen-2025-lexical,
    title = "Lexical Semantic Change Annotation with Large Language Models",
    author = "Hagen, Thora",
    editor = "Kazantseva, Anna  and
      Szpakowicz, Stan  and
      Degaetano-Ortlieb, Stefania  and
      Bizzoni, Yuri  and
      Pagel, Janis",
    booktitle = "Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.latechclfl-1.16/",
    doi = "10.18653/v1/2025.latechclfl-1.16",
    pages = "172--178",
    ISBN = "979-8-89176-241-1",
    abstract = "This paper explores the application of state-of-the-art large language models (LLMs) to the task of lexical semantic change annotation (LSCA) using the historical German DURel dataset. We evaluate five LLMs, and investigate whether retrieval-augmented generation (RAG) with historical encyclopedic knowledge enhances results. Our findings show that the Llama3.3 model achieves comparable performance to GPT-4o despite significant parameter differences, while RAG marginally improves predictions for smaller models but hampers performance for larger ones. Further analysis suggests that our additional context benefits nouns more than verbs and adjectives, demonstrating the nuances of integrating external knowledge for semantic tasks."
}