FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models
<|reference_start|>FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models: Despite advancements in large language models (LLMs), non-factual responses remain prevalent. Unlike extensive studies on post-hoc detection of such responses, this work studies non-factuality prediction (NFP), aiming to predict whether an LLM will generate a non-factual response to a question before the generation process. Previous efforts on NFP have demonstrated LLMs' awareness of their internal knowledge, but they still face challenges in efficiency and transferability. In this work, we propose a lightweight NFP model named Factuality Lens (FacLens), which effectively probes hidden representations of questions for the NFP task. Besides, we discover that hidden question representations sourced from different LLMs exhibit similar NFP patterns, which enables the transferability of FacLens across LLMs to reduce development costs. Extensive experiments highlight FacLens's superiority in both effectiveness and efficiency.<|reference_end|>
@article{wang2024faclens:,
    title={FacLens: Transferable Probe for Foreseeing Non-Factuality in Large
  Language Models},
    author={Yanling Wang, Haoyang Li, Hao Zou, Jing Zhang, Xinlei He, Qi Li, Ke Xu},
    journal={arXiv preprint arXiv:2406.05328},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05328},
    primaryClass={cs.CL cs.LG}
}