While Language Agents have achieved promising success by placing Large Language Models at the core of a more versatile design that dynamically interacts with the external world, the existing approaches neglect the notion of uncertainty during these interactions. We present the Uncertainty-Aware Language Agent (UALA), a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification. Compared with other well-known counterparts like ReAct, our extensive experiments across 3 representative tasks (HotpotQA, StrategyQA, MMLU) and various LLM sizes demonstrate that UALA brings a significant improvement of performance, while having a substantially lower reliance on the external world (i.e., reduced number of tool calls and tokens). Our analyses provide various insights including the great potential of UALA compared with agent fine-tuning, and underscore the unreliability of verbalised confidence of LLMs as a proxy for uncertainty.
@article{arxiv.2401.14016,
title = {Towards Uncertainty-Aware Language Agent},
author = {Jiuzhou Han and Wray Buntine and Ehsan Shareghi},
journal= {arXiv preprint arXiv:2401.14016},
year = {2024}
}
Comments
Our code and data are at https://uala-agent.github.io. (accepted to ACL 2024 Findings). arXiv admin note: text overlap with arXiv:2310.05915