  Uncertainty estimation is a significant issue for current large language
models (LLMs) that are generally poorly calibrated and over-confident,
especially with reinforcement learning from human feedback (RLHF). Unlike
humans, whose decisions and confidences not only stem from intrinsic beliefs
but can also be adjusted through daily observations, existing calibration
methods for LLMs focus on estimating or eliciting individual confidence without
taking full advantage of the "Collective Wisdom": the interaction among
multiple LLMs that can collectively improve both accuracy and calibration. In
this work, we propose Collaborative Calibration, a post-hoc training-free
calibration strategy that leverages the collaborative and expressive
capabilities of multiple tool-augmented LLM agents in a simulated group
deliberation process. We demonstrate the effectiveness of Collaborative
Calibration on generative QA tasks across various domains, showing its
potential in harnessing the rationalization of collectively calibrated
confidence assessments and improving the reliability of model predictions.
