SOURCE-LINKED INTELLIGENCE
A Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs
A critical challenge in deploying Large Language Models (LLMs) is developing reliable mechanisms to estimate their confidence, enabling systems to determine when to trust model outputs versus seek human intervention. We present a Calibrated Reflection approach for enhancing confidence estimation in LLMs, a framework that combines structured reasoning with distance-aware calibration technique. Our approach introduces three key innovations: (1) a Maximum Confidence Selection (MCS) method that comprehensively evaluates confidence across all possible labels, (2) a reflection-based prompting mechan
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T22:52:01.000Z
First collected: 2026-09-21T04:31:57.454Z. This is not the publication date.