@inproceedings{wang-etal-2026-refact,
    title = "{R}e{FACT}: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations",
    author = "Wang, Yindong  and
      Prei{\ss}, Martin  and
      Bugue{\~n}o, Margarita  and
      Hoffbauer, Jan Vincent  and
      Ghajar, Abdullatif  and
      Buz, Tolga  and
      de Melo, Gerard",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.381/",
    pages = "8174--8187",
    ISBN = "979-8-89176-380-7",
    abstract = "The mechanisms underlying scientific confabulation in Large Language Models (LLMs) remain poorly understood. We introduce ReFACT, a benchmark of 1,001 expert-annotated question-answer pairs with span-level error annotations derived from Reddit{'}s r/AskScience. Evaluating 9 state-of-the-art LLMs reveals two critical limitations. First, models exhibit a dominant salient distractor failure mode: 61{\%} of incorrect span predictions are semantically unrelated to actual errors. Crucially, this pattern persists across all model scales (1B to 70B), indicating a fundamental semantic grounding deficit that scaling alone fails to resolve. Second, we find that comparative judgment is paradoxically harder than independent detection{--}even GPT-4o{'}s F1 score drops from 0.67 to 0.53 when comparing answers side-by-side. These findings directly challenge the reliability of LLM-as-Judge paradigms for scientific factuality. Code and data are released at https://github.com/ddz5431/ReFACT."
}