CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?
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.
6911c8bc830c57fd4c796124
Qing Zong
6911c8bc830c57fd4c796125
Jiayu Liu
claimed_verified
2025-11-10T21:02:59.143Z
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/avatars/eb9a0441986be50274c5e661f7039e2c.svg
Jeff
JiayuJeff
user
JiayuJeff
6911c8bc830c57fd4c796126
Tianshi Zheng
6911c8bc830c57fd4c796127
Chunyang Li
6911c8bc830c57fd4c796128
Baixuan Xu
6911c8bc830c57fd4c796129
Haochen Shi
6911c8bc830c57fd4c79612a
Weiqi Wang
6911c8bc830c57fd4c79612b
Zhaowei Wang
6911c8bc830c57fd4c79612c
Chunkit Chan
6911c8bc830c57fd4c79612d
Yangqiu Song
CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?
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.
Natural language critiques improve confidence calibration in LLMs, with CritiCal training method outperforming other approaches and enhancing reliability.
Large Language Models confidence calibration natural language critiques self-critique CritiCal confidence assessment uncertainty multiple-choice tasks open-ended scenarios complex reasoning tasks out-of-distribution settings