@inproceedings{wang-etal-2025-faclens,
    title = "{F}ac{L}ens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models",
    author = "Wang, Yanling  and
      Li, Haoyang  and
      Zou, Hao  and
      Zhang, Jing  and
      He, Xinlei  and
      Li, Qi  and
      Xu, Ke",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.937/",
    pages = "18574--18593",
    ISBN = "979-8-89176-332-6",
    abstract = "Despite advancements in large language models (LLMs), non-factual responses still persist in fact-seeking question answering. Unlike extensive studies on post-hoc detection of these responses, this work studies non-factuality prediction (NFP), predicting whether an LLM will generate a non-factual response prior to the response generation. Previous NFP methods have shown LLMs' awareness of their knowledge, but they face challenges in terms of efficiency and transferability. In this work, we propose a lightweight model named Factuality Lens (FacLens), which effectively probes hidden representations of fact-seeking questions for the NFP task. Moreover, we discover that hidden question representations sourced from different LLMs exhibit similar NFP patterns, enabling the transferability of FacLens across different LLMs to reduce development costs. Extensive experiments highlight FacLens{'}s superiority in both effectiveness and efficiency."
}