@inproceedings{huang-etal-2025-ucsc,
    title = "{UCSC} at {S}em{E}val-2025 Task 3: Context, Models and Prompt Optimization for Automated Hallucination Detection in {LLM} Output",
    author = "Huang, Sicong  and
      He, Jincheng  and
      Huang, Shiyuan  and
      Anandan, Karthik Raja  and
      Chakraborty, Arkajyoti  and
      Lane, Ian",
    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-1.257/",
    pages = "1981--1992",
    ISBN = "979-8-89176-273-2",
    abstract = "Hallucinations pose a significant challenge for large language models when answering knowledge-intensive queries. As LLMs become more widely adopted, it is crucial not only to detect if hallucinations occur but also to pinpoint where they arise. SemEval 2025 Task 3, Mu-SHROOM: Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes, is a recent effort in this direction. This paper describes our solution to the shared task. We propose a framework that first retrieves relevant context, next identifies false content from the answer, and finally maps them back to spans. The process is further enhanced by automatically optimizing prompts. Our system achieves the highest overall performance, ranking {\#}1 in average position across all languages."
}