从被动度量到主动信号：不确定性量化在大语言模型中的角色演变 / From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
1️⃣ 一句话总结
这篇综述论文指出，将大语言模型输出的不确定性从一种被动的诊断指标，转变为一种能实时指导模型行为的主动控制信号，是提升其在高风险领域可靠性和可信度的关键趋势。
arXiv 提交日期: 2026-01-22
Abstract - From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
While Large Language Models (LLMs) show remarkable capabilities, their unreliability remains a critical barrier to deployment in high-stakes domains. This survey charts a functional evolution in addressing this challenge: the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior. We demonstrate how uncertainty is leveraged as an active control signal across three frontiers: in \textbf{advanced reasoning} to optimize computation and trigger self-correction; in \textbf{autonomous agents} to govern metacognitive decisions about tool use and information seeking; and in \textbf{reinforcement learning} to mitigate reward hacking and enable self-improvement via intrinsic rewards. By grounding these advancements in emerging theoretical frameworks like Bayesian methods and Conformal Prediction, we provide a unified perspective on this transformative trend. This survey provides a comprehensive overview, critical analysis, and practical design patterns, arguing that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
源自 arXiv: 2601.15690