@inproceedings{zhang-etal-2026-passive,
title = "From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models",
author = "Zhang, Jiaxin and
Cui, Wendi and
Li, Zhuohang and
Huang, Lifu and
Malin, Bradley A. and
Xiong, Caiming and
Wu, Chien-Sheng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2064/",
pages = "41525--41544",
ISBN = "979-8-89176-395-1",
abstract = "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."
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%0 Conference Proceedings
%T From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
%A Zhang, Jiaxin
%A Cui, Wendi
%A Li, Zhuohang
%A Huang, Lifu
%A Malin, Bradley A.
%A Xiong, Caiming
%A Wu, Chien-Sheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-passive
%X 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.
%U https://aclanthology.org/2026.findings-acl.2064/
%P 41525-41544
Markdown (Informal)
[From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models](https://aclanthology.org/2026.findings-acl.2064/) (Zhang et al., Findings 2026)
ACL
- Jiaxin Zhang, Wendi Cui, Zhuohang Li, Lifu Huang, Bradley A. Malin, Caiming Xiong, and Chien-Sheng Wu. 2026. From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41525–41544, San Diego, California, United States. Association for Computational Linguistics.