@inproceedings{zhao-etal-2025-uncertainty,
title = "Uncertainty Propagation on {LLM} Agent",
author = "Zhao, Qiwei and
Li, Dong and
Liu, Yanchi and
Cheng, Wei and
Sun, Yiyou and
Oishi, Mika and
Osaki, Takao and
Matsuda, Katsushi and
Yao, Huaxiu and
Zhao, Chen and
Chen, Haifeng and
Zhao, Xujiang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.302/",
doi = "10.18653/v1/2025.acl-long.302",
pages = "6064--6073",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) integrated into multi-step agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multi-step decision-making process and the dynamic interactions between agents and their environments. To address these limitations, we propose SAUP (Situation Awareness Uncertainty Propagation), a novel framework that propagates uncertainty through each step of an LLM-based agent{'}s reasoning process. SAUP incorporates situational awareness by assigning situational weights to each step{'}s uncertainty during the propagation. Our method, compatible with various one-step uncertainty estimation techniques, provides a comprehensive and accurate uncertainty measure. Extensive experiments on benchmark datasets demonstrate that SAUP significantly outperforms existing state-of-the-art methods, achieving up to 20{\%} improvement in AUROC."
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<abstract>Large language models (LLMs) integrated into multi-step agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multi-step decision-making process and the dynamic interactions between agents and their environments. To address these limitations, we propose SAUP (Situation Awareness Uncertainty Propagation), a novel framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process. SAUP incorporates situational awareness by assigning situational weights to each step’s uncertainty during the propagation. Our method, compatible with various one-step uncertainty estimation techniques, provides a comprehensive and accurate uncertainty measure. Extensive experiments on benchmark datasets demonstrate that SAUP significantly outperforms existing state-of-the-art methods, achieving up to 20% improvement in AUROC.</abstract>
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%0 Conference Proceedings
%T Uncertainty Propagation on LLM Agent
%A Zhao, Qiwei
%A Li, Dong
%A Liu, Yanchi
%A Cheng, Wei
%A Sun, Yiyou
%A Oishi, Mika
%A Osaki, Takao
%A Matsuda, Katsushi
%A Yao, Huaxiu
%A Zhao, Chen
%A Chen, Haifeng
%A Zhao, Xujiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-etal-2025-uncertainty
%X Large language models (LLMs) integrated into multi-step agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multi-step decision-making process and the dynamic interactions between agents and their environments. To address these limitations, we propose SAUP (Situation Awareness Uncertainty Propagation), a novel framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process. SAUP incorporates situational awareness by assigning situational weights to each step’s uncertainty during the propagation. Our method, compatible with various one-step uncertainty estimation techniques, provides a comprehensive and accurate uncertainty measure. Extensive experiments on benchmark datasets demonstrate that SAUP significantly outperforms existing state-of-the-art methods, achieving up to 20% improvement in AUROC.
%R 10.18653/v1/2025.acl-long.302
%U https://aclanthology.org/2025.acl-long.302/
%U https://doi.org/10.18653/v1/2025.acl-long.302
%P 6064-6073
Markdown (Informal)
[Uncertainty Propagation on LLM Agent](https://aclanthology.org/2025.acl-long.302/) (Zhao et al., ACL 2025)
ACL
- Qiwei Zhao, Dong Li, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, and Xujiang Zhao. 2025. Uncertainty Propagation on LLM Agent. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6064–6073, Vienna, Austria. Association for Computational Linguistics.