@inproceedings{hahn-2025-jim,
    title = "Jim at {S}em{E}val-2025 Task 5: Multilingual {BERT} Ensemble",
    author = "Hahn, Jim",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.313/",
    pages = "2407--2412",
    ISBN = "979-8-89176-273-2",
    abstract = "The SemEval-2025 Task 5 calls for the utilization of LLM capabilities to apply controlled subject labels to record descriptions in the multilingual library collection of the German National Library of Science and Technology. The multilingual BERT ensemble system described herein produces subject labels for various record types, including articles, books, conference papers, reports, and theses. Results indicate that for English language article records, bidirectional encoder-only LLMs can achieve high recall in automated subject assignment."
}