@inproceedings{chen-etal-2025-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task3: Leveraging Zero-Shot Learning for Halluciantion Detection",
    author = "Chen, Shen  and
      Wang, Jin  and
      Zhang, Xuejie",
    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/transition-to-people-yaml/2025.semeval-1.5/",
    pages = "28--33",
    ISBN = "979-8-89176-273-2",
    abstract = "This study reports the YNU-HPCC team{'}s participation in SemEval-2025 shared task 3, which focuses on detecting hallucination spans in multilingual instruction-tuned LLM outputs. This task differs from typical hallucination detection tasks in that it does not require identifying the entire response or pinpointing which sentences contain hallucinations generated by the LLM. Instead, the task focuses on detecting hallucinations at the character level. In addition, this task differs from typical hallucination detection based on binary classification. It requires not only identifying hallucinations but also assigning a likelihood score to indicate how likely each part of the model output is hallucinatory. Our approach combines Retrieval-Augmented Generation (RAG) and zero-shot methods, guiding LLMs to detect and extract hallucination spans using external knowledge. The proposed system achieved first place in Chinese and fifteenth place in English for track3."
}