@inproceedings{carla-uban-2025-scibert,
    title = "{S}ci{BERT} Meets Contrastive Learning: A Solution for Scientific Hallucination Detection",
    author = "Carla, Crivoi  and
      Uban, Ana Sabina",
    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-1.32/",
    doi = "10.18653/v1/2025.sdp-1.32",
    pages = "336--343",
    ISBN = "979-8-89176-265-7",
    abstract = "As AI systems become more involved in scientific research, there is growing concern about the accuracy of their outputs. Tools powered by large language models can generate summaries and answers that appear well-formed, but sometimes include claims that are not actually supported by the cited references. In this paper, we focus on identifying these hallucinated claims. We propose a system built on SciBERT and contrastive learning to detect whether a scientific claim can be inferred from the referenced content. Our method was evaluated in the SciHal 2025 shared task, which includes both coarse and fine-grained hallucination labels. The results show that our model performs well on supported and clearly unsupported claims, but struggles with ambiguous or low-resource categories. These findings highlight both the promise and the limitations of current models in improving the trustworthiness of AI-generated scientific content."
}