On Verbalized Confidence Scores for LLMs

Understand

The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness.

• Uncertainty quantification for LLMs can establish more human trust into their responses, but also allows LLM agents to make more informed decisions based on each other's uncertainty.
• To estimate the uncertainty in a response, internal token logits, task-specific proxy models, or sampling of multiple responses are commonly used.
• This work focuses on asking the LLM itself to verbalize its uncertainty with a confidence score as part of its output tokens, which is a promising way for prompt- and model-agnostic uncertainty quantification with low overhead.