Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts
<|reference_start|>Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts: In this work, we show the pre-trained language models return distinguishable generation probability and uncertainty distribution to unfaithfully hallucinated texts, regardless of their size and structure. By examining 24 models on 6 data sets, we find out that 88-98% of cases return statistically significantly distinguishable generation probability and uncertainty distributions. Using this general phenomenon, we showcase a hallucination-reducing training algorithm. Our algorithm outperforms other baselines by achieving higher faithfulness metrics while maintaining sound general text quality measures.<|reference_end|>
@article{cha2024pre-trained,
    title={Pre-trained Language Models Return Distinguishable Probability
  Distributions to Unfaithfully Hallucinated Texts},
    author={Taehun Cha and Donghun Lee},
    journal={arXiv preprint arXiv:2409.16658},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16658},
    primaryClass={cs.CL}
}