Abstract
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.- Anthology ID:
- 2024.findings-acl.398
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6662–6685
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.398
- DOI:
- Cite (ACL):
- Jiuzhou Han, Wray Buntine, and Ehsan Shareghi. 2024. Towards Uncertainty-Aware Language Agent. In Findings of the Association for Computational Linguistics ACL 2024, pages 6662–6685, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Towards Uncertainty-Aware Language Agent (Han et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.398.pdf