@inproceedings{caballero-etal-2025-nlpuned,
    title = "{N}lp{U}ned at {S}em{E}val-2025 Task 10: Beyond Training: A Taxonomy-Guided Approach to Role Classification Using {LLM}s",
    author = "Caballero, Alberto  and
      Rodrigo, Alvaro  and
      Centeno, Roberto",
    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-1.42/",
    pages = "296--301",
    ISBN = "979-8-89176-273-2",
    abstract = "The paper presents a taxonomy-guided approach to role classification in news articles using Large Language Models (LLMs). Instead of traditional model training, the system employs zero-shot and few-shot prompting strategies, leveraging structured taxonomies and contextual cues for classification. The study evaluates hierarchical and single-step classification approaches, finding that a unified, single-step model with contextual preprocessing achieves the best performance. The research underscores the importance of input structuring and classification strategy in optimizing LLM performance for real-world applications."
}