Multi-Perspective Consistency Enhances Confidence Estimation in Large
  Language Models
Pei Wang Yejie Wang Muxi Diao Keqing He Guanting Dong Weiran Xu
In the deployment of large language models (LLMs), accurate confidence
estimation is critical for assessing the credibility of model predictions.
However, existing methods often fail to overcome the issue of overconfidence on
incorrect answers. In this work, we focus on improving the confidence
estimation of large language models. Considering the fragility of
self-awareness in language models, we introduce a Multi-Perspective Consistency
(MPC) method. We leverage complementary insights from different perspectives
within models (MPC-Internal) and across different models (MPC-Across) to
mitigate the issue of overconfidence arising from a singular viewpoint. The
experimental results on eight publicly available datasets show that our MPC
achieves state-of-the-art performance. Further analyses indicate that MPC can
mitigate the problem of overconfidence and is effectively scalable to other
models.