@inproceedings{khoa-van-2025-p,
    title = "{A}.{M}.{P} at {S}ci{H}al2025: Automated Hallucination Detection in Scientific Content via {LLM}s and Prompt Engineering",
    author = "Khoa, Le Nguyen Anh  and
      V{\u{a}}n, Th{\`i}n {\DJ}ặng",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sdp-1.31/",
    doi = "10.18653/v1/2025.sdp-1.31",
    pages = "328--335",
    ISBN = "979-8-89176-265-7",
    abstract = "This paper presents our system developed for SciHal2025: Hallucination Detection for Scientific Content. The primary goal of this task is to detect hallucinated claims based on the corresponding reference. Our methodology leverages strategic prompt engineering to enhance LLMs' ability to accurately distinguish between factual assertions and hallucinations in scientific contexts. Moreover, we discovered that aggregating the fine-grained classification results from the more complex subtask (subtask 2) into the simplified label set required for the simpler subtask (subtask 1) significantly improved performance compared to direct classification for subtask 1. This work contributes to the development of more reliable AI-powered research tools by providing a systematic framework for hallucination detection in scientific content."
}