Graph-based Confidence Calibration for Large Language Models

Yukun Li, Sijia Wang, Lifu Huang, Liping Liu

Abstract: Reliable confidence estimation is essential for enhancing the trustworthiness of large language models (LLMs), especially in high-stakes scenarios. Despite its importance, accurately estimating confidence in LLM responses remains a significant challenge. In this work, we propose using an auxiliary learning model to assess response correctness based on the self-consistency of multiple outputs generated by the LLM. Our method builds a consistency graph to represent the agreement among multiple responses and uses a graph neural network (GNN) to estimate the likelihood that each response is correct. Experiments demonstrate that this method has strong calibration performance on various benchmark datasets and generalizes well to out-of-domain cases.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Tal_Schuster1

Submission Number: 4032