This research introduces a novel betting game framework that trains language models to accurately express confidence in their answers, addressing a critical challenge in AI safety and trustworthiness.

Key Innovations:

• Uses reinforcement learning to penalize both over-confidence and under-confidence
• Frames confidence calibration as a strategic betting game
• Develops rewards that encourage honest uncertainty expression
• Enhances model reliability without sacrificing performance

Security Implications: Well-calibrated confidence scores allow organizations to identify potentially harmful AI responses, establish appropriate trust levels, and implement safer AI systems in high-stakes environments.

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