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.

翻译：幻觉是大型语言模型面临的主要挑战，阻碍了其在各领域的广泛应用。不确定性估计可用于减轻幻觉造成的损害。人类的怀疑情绪可能有助于增强模型的自我估计能力。受此启发，我们提出了一种称为怀疑建模的新方法。该方法通过结合词元信息和逻辑值进行自我估计的形式化实现。我们构建了怀疑情绪感知数据，执行持续预训练，随后对大型语言模型进行微调，从而提升其自我估计能力。实验结果表明，新方法能有效增强模型的不确定性估计能力，并通过域外实验验证了其在其他任务上的泛化性能。