ConfTuner: Training Large Language Models to Express Their Confidence Verbally

Understand

Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as science, law, and healthcare, where accurate expressions of uncertainty are essential for reliability and trust.

• However, current LLMs are often observed to generate incorrect answers with high confidence, a phenomenon known as "overconfidence".
• Recent efforts have focused on calibrating LLMs' verbalized confidence: i.e., their expressions of confidence in text form, such as "I am 80% confident that...".
• Existing approaches either rely on prompt engineering or fine-tuning with heuristically generated uncertainty estimates, both of which have limited effectiveness and generalizability.