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 advanced reasoning to optimize computation and trigger self-correction; in autonomous agents to govern metacognitive decisions about tool use and information seeking; and in 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.
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Jiaxin Zhang
6973c19c5d41524304c131aa
Wendi Cui
6973c19c5d41524304c131ab
Zhuohang Li
6973c19c5d41524304c131ac
Lifu Huang
6973c19c5d41524304c131ad
Bradley Malin
6973c19c5d41524304c131ae
Caiming Xiong
6973c19c5d41524304c131af
Chien-Sheng Wu
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 advanced reasoning to optimize computation and trigger self-correction; in autonomous agents to govern metacognitive decisions about tool use and information seeking; and in 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.
Large language models face reliability challenges that are being addressed through uncertainty as an active control signal across advanced reasoning, autonomous agents, and reinforcement learning, supported by theoretical frameworks like Bayesian methods and conformal prediction.
Large Language Models uncertainty active control signal advanced reasoning autonomous agents reinforcement learning Bayesian methods Conformal Prediction reward hacking self-improvement intrinsic rewards