Uncertainty Distillation: Teaching Language Models to Express Semantic Confidence

Understand

As large language models (LLMs) are increasingly used for factual question-answering, it becomes more important for LLMs to have the capability to communicate the likelihood that their answer is correct.

• For these verbalized expressions of uncertainty to be meaningful, they should reflect the error rates at the expressed level of confidence.
• However, when prompted to express confidence, the error rates of current LLMs are inconsistent with their communicated confidences, highlighting the need for uncertainty quantification methods.
• Many prior methods calculate lexical uncertainty, estimating a model's confidence in the specific string it generated.