SAUP: Situation Awareness Uncertainty Propagation on LLM Agent

• URL: http://arxiv.org/abs/2412.01033v1
• Date: Mon, 02 Dec 2024 01:31:13 GMT
• Title: SAUP: Situation Awareness Uncertainty Propagation on LLM Agent
• Authors: Qiwei Zhao, Xujiang Zhao, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Haifeng Chen,
• Abstract summary: Large language models (LLMs) integrated into multistep agent systems enable complex decision-making processes across various applications. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multistep decision-making process and the dynamic interactions between agents and their environments. We propose SAUP, a novel framework that propagates uncertainty through each step of an LLM-based agent's reasoning process.
• Score: 52.444674213316574
• License:
• Abstract: Large language models (LLMs) integrated into multistep 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 multistep 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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