Rewarding Doubt: A Reinforcement Learning Approach to Confidence Calibration of Large Language Models

• URL: http://arxiv.org/abs/2503.02623v2
• Date: Wed, 05 Mar 2025 15:23:16 GMT
• Title: Rewarding Doubt: A Reinforcement Learning Approach to Confidence Calibration of Large Language Models
• Authors: Paul Stangel, David Bani-Harouni, Chantal Pellegrini, Ege Özsoy, Kamilia Zaripova, Matthias Keicher, Nassir Navab,
• Abstract summary: A safe and trustworthy use of Large Language Models (LLMs) requires an accurate expression of confidence in their answers.<n>We introduce a novel Reinforcement Learning (RL) approach for LLM calibration that fine-tunes LLMs to elicit calibrated confidence estimations in their answers to factual questions.
• Score: 34.59785123314865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A safe and trustworthy use of Large Language Models (LLMs) requires an accurate expression of confidence in their answers. We introduce a novel Reinforcement Learning (RL) approach for LLM calibration that fine-tunes LLMs to elicit calibrated confidence estimations in their answers to factual questions. We model the problem as a betting game where the model predicts a confidence score together with every answer, and design a reward function that penalizes both over and under-confidence. We prove that under our reward design an optimal policy would result in a perfectly calibrated confidence estimation. Our experiments demonstrate significantly improved confidence calibration and generalization to new tasks without re-training, indicating that our approach teaches a general confidence awareness. This approach enables the training of inherently calibrated LLMs.

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