Alleviating Hallucinations in Large Language Models with Scepticism Modeling
<|reference_start|>Alleviating Hallucinations in Large Language Models with Scepticism Modeling: Hallucinations is a major challenge for large language models (LLMs), prevents adoption in diverse fields. Uncertainty estimation could be used for alleviating the damages of hallucinations. The skeptical emotion of human could be useful for enhancing the ability of self estimation. Inspirited by this observation, we proposed a new approach called Skepticism Modeling (SM). This approach is formalized by combining the information of token and logits for self estimation. We construct the doubt emotion aware data, perform continual pre-training, and then fine-tune the LLMs, improve their ability of self estimation. Experimental results demonstrate this new approach effectively enhances a model's ability to estimate their uncertainty, and validate its generalization ability of other tasks by out-of-domain experiments.<|reference_end|>
@article{wu2024alleviating,
    title={Alleviating Hallucinations in Large Language Models with Scepticism
  Modeling},
    author={Yetao Wu, Yihong Wang, Teng Chen, Chenxi Liu, Ningyuan Xi, Qingqing
  Gu, Hongyang Lei, Zhonglin Jiang, Yong Chen, Luo Ji},
    journal={arXiv preprint arXiv:2409.06601},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06601},
    primaryClass={cs.CL cs.LG}
}