@inproceedings{hong-etal-2025-ncl,
    title = "{NCL}-{U}o{R} at {S}em{E}val-2025 Task 3: Detecting Multilingual Hallucination and Related Observable Overgeneration Text Spans with Modified {R}ef{C}hecker and Modified {S}efl{C}heck{GPT}",
    author = "Hong, Jiaying  and
      Markchom, Thanet  and
      Xu, Jianfei  and
      Wu, Tong  and
      Liang, Huizhi",
    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-1.39/",
    pages = "271--279",
    ISBN = "979-8-89176-273-2",
    abstract = "SemEval-2025 Task 3 (Mu-SHROOM) focuses on detecting hallucinations in content generated by various large language models (LLMs) across multiple languages. This task involves not only identifying the presence of hallucinations but also pinpointing their specific occurrences. To tackle this challenge, this study introduces two methods: modified RefChecker and modified SelfCheckGPT. The modified RefChecker integrates prompt-based factual verification into References, structuring them as claim-based tests rather than single external knowledge sources. The modified SelfCheckGPT {\textasciitilde}incorporates external knowledge to overcome its reliance on internal knowledge. In addition, both methods' original prompt designs are enhanced to identify hallucinated words within LLM-generated texts. Experimental results demonstrate the effectiveness of the approach, achieving a high ranking on the test dataset in detecting hallucinations across various languages, with an average IoU of 0.5310 and an average COR of 0.5669."
}