CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?
Authors: name: Qing Zong, name: Jiayu Liu, name: Tianshi Zheng, name: Chunyang Li, name: Baixuan Xu, name: Haochen Shi, name: Weiqi Wang, name: Zhaowei Wang, name: Chunkit Chan, name: Yangqiu Song

Accurate confidence calibration in Large Language Models (LLMs) is critical
for safe use in high-stakes domains, where clear verbalized confidence enhances
user trust. Traditional methods that mimic reference confidence expressions
often fail to capture the reasoning needed for accurate confidence assessment.
We propose natural language critiques as a solution, ideally suited for
confidence calibration, as precise gold confidence labels are hard to obtain
and often require multiple generations. This paper studies how natural language
critiques can enhance verbalized confidence, addressing: (1) What to critique:
uncertainty (question-focused) or confidence (answer-specific)? Analysis shows
confidence suits multiple-choice tasks, while uncertainty excels in open-ended
scenarios. (2) How to critique: self-critique or critique calibration training?
We propose Self-Critique, enabling LLMs to critique and optimize their
confidence beyond mere accuracy, and CritiCal, a novel Critique Calibration
training method that leverages natural language critiques to improve confidence
calibration, moving beyond direct numerical optimization. Experiments show that
CritiCal significantly outperforms Self-Critique and other competitive
baselines, even surpassing its teacher model, GPT-4o, in complex reasoning
tasks. CritiCal also shows robust generalization in out-of-distribution
settings, advancing LLM's reliability.