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LLM research tackles uncertainty in function calls and system propagation

作者 PulseAugur 编辑部 · [2 sources] ·

Two new research papers explore the critical issue of uncertainty in Large Language Models (LLMs). The first paper investigates uncertainty quantification methods specifically for LLM function-calling, finding that simple single-sample methods can be effective and can be improved by analyzing output structure. The second paper addresses uncertainty propagation within complex LLM-based systems, proposing a framework to understand how errors can compound across various system components and processes. AI

影响 These papers highlight the need for better uncertainty management in LLM systems, crucial for reliable deployment in real-world applications.

排序理由 Two academic papers published on arXiv discussing uncertainty in LLMs.

在 arXiv cs.CL 阅读 →

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LLM research tackles uncertainty in function calls and system propagation

报道来源 [2]

  1. arXiv cs.CL TIER_1 · Zihuiwen Ye, Lukas Aichberger, Michael Kirchhof, Sinead Williamson, Luca Zappella, Yarin Gal, Arno Blaas, Adam Golinski ·

    Uncertainty Quantification for LLM Function-Calling

    arXiv:2604.22985v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling paradigm, a widely used approach for equipping LLMs with tool-use capabilities. How…

  2. arXiv cs.AI TIER_1 · Boming Xia, Liming Zhu, Erdun Gao, Qinghua Lu, Minhui Xue, Dino Sejdinovic ·

    Uncertainty Propagation in LLM-Based Systems

    arXiv:2604.23505v1 Announce Type: cross Abstract: Uncertainty in large language model (LLM)-based systems is often studied at the level of a single model output, yet deployed LLM applications are compound systems in which uncertainty is transformed and reused across model interna…

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