One important approach to improving the reliability of large language models (LLMs) is to provide accurate confidence estimations regarding the correctness of their answers. However, developing a well-calibrated confidence estimation model is challenging, as mistakes made by LLMs can be difficult to detect. We propose a novel method combining the LLM's self-consistency with labeled data and training an auxiliary model to estimate the correctness of its responses to questions. This auxiliary model predicts the correctness of responses based solely on their consistent information. To set up the learning problem, we use a weighted graph to represent the consistency among the LLM's multiple responses to a question. Correctness labels are assigned to these responses based on their similarity to the correct answer. We then train a graph neural network to estimate the probability of correct responses. Experiments demonstrate that the proposed approach substantially outperforms several of the most recent methods in confidence calibration across multiple widely adopted benchmark datasets. Furthermore, the proposed approach significantly improves the generalization capability of confidence calibration on out-of-domain (OOD) data.

本文解决了大型语言模型（LLMs）在提供准确自信度估计时的可靠性问题。研究提出了一种新颖的方法，通过结合LLM的自一致性与标记数据，训练一个辅助模型来预测其回答的正确性，最终实验表明该方法在多个基准数据集上明显优于现有自信度校准方法，并显著提升了模型在域外数据上的泛化能力。

基于图的自信度校准方法用于大型语言模型