Abstract
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.
Abstract (translated)
不确定性估计是当前大型语言模型（LLMs）的一个显著问题，尤其是对于从人类反馈中进行强化学习（RLHF）的情况。与人类不同，后者不仅基于内在信念，还可以通过日常观察进行调整。现有的LLM calibration方法主要关注估计或激发单个模型的置信，而没有充分利用“集体智慧”这一概念：多个LLM之间相互作用的集体改进 both准确性和校准。在本文中，我们提出了协作校准，一种无需进行后训练的校准策略，它利用了模拟群体讨论过程中多个增强型LLM代理的协作和表现特性。我们展示了协作校准在各种领域的生成式QA任务上的有效性，表明了其在利用集体校准置信评估的合理性以及提高模型预测可靠性的潜力。
URL
https://arxiv.org/abs/2404.09127
