1. SAUP: Situation Awareness Uncertainty Propagation on LLM Agent
- Author
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Zhao, Qiwei, Zhao, Xujiang, Liu, Yanchi, Cheng, Wei, Sun, Yiyou, Oishi, Mika, Osaki, Takao, Matsuda, Katsushi, Yao, Huaxiu, and Chen, Haifeng
- Subjects
Computer Science - Computation and Language ,Computer Science - Machine Learning - 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.
- Published
- 2024