Language Model Calibration

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Language model calibration asks whether a model’s stated or computed confidence corresponds to how often its answers are correct, so systems can decide when to answer, retrieve evidence, defer, or abstain. Researchers estimate confidence from verbal reports, token probabilities, self-consistency, or hidden representations, then improve it with post-hoc methods such as temperature scaling and isotonic regression, conformal prediction, or training methods including supervised fine-tuning and reinforcement learning. Evaluations commonly use question answering and mathematical reasoning benchmarks, and increasingly test retrieval routing, model judges, medical tasks, and performance under domain or prompt shifts. A central tension is that calibration can change with the elicitation protocol and deployment context, and well-calibrated probabilities do not necessarily rank errors well or guarantee safe decisions.

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386 papers67 papers in the last four weeks, up 40% on the four weeks before. 0.4% of all new papers.

From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
Mar 6, 2026Azza Jenane, Nassim Walha, Lukas Kuhn +1LLM Uncertainty Estimation Language Model Calibration
German Cancer Research Center (DKFZ) German Cancer Consortium (DKTK) Goethe University Frankfurt, Germany
Believe Your Model: Distribution-Guided Confidence Calibration
Mar 4, 2026Xizhong Yang, Haotian Zhang, Huiming Wang +1Language Model Calibration Confidence Estimation in Language Models
Southeast University · Kuaishou Technology
Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning
Mar 4, 2026Chuang Zhang, Zizhen Zhu, Yihao Wei +5Language Model Cascading Efficient Language Model Reasoning
Amazon Web Services · Tsinghua University
Frontier Models Can Take Actions at Low Probabilities
Mar 2, 2026Alex Serrano, Wen Xing, David Lindner +1Language Model Safety Evaluation Strategic Reasoning Risks in Language Models
MATS† · Google DeepMind
Self-Anchoring Calibration Drift in Large Language Models: How Multi-Turn Conversations Reshape Model Confidence
Mar 1, 2026HarshavardhanConfidence Estimation in Language Models Language Model Calibration
Independent Researcher
HypeLoRA: Hyper-Network-Generated LoRA Adapters for Calibrated Language Model Fine-Tuning
Mar 1, 2026Bartosz Trojan, Filip GębalaLow-Rank Adaptation Language Model Calibration
Upper-Secondary Schools of Communications in Cracow
Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains
Mar 1, 2026Manil Shrestha, Edward KimNamed Entity Recognition Language Model Calibration
Department of Computer Science, Drexel University, Philadelphia, USA
Rubric-Guided Fine-tuning of SpeechLLMs for Multi-Aspect, Multi-Rater L2 Reading-Speech Assessment
Feb 28, 2026Aditya Kamlesh Parikh, Cristian Tejedor-Garcia, Catia Cucchiarini +1Speech Quality Assessment Speech Language Models
Centre for Language Studies, Radboud University, The Netherlands
Empirical Characterization of Inference-Time Elicited Probability Transformations in Large Language Models
Feb 26, 2026Mike Farmer, Abhinav Kochar, Yugyung LeeLLM Inference Belief Updating in LLMs
Bloch School of Management, Regnier Institute for Entrepreneurship & Innovation, University of Missouri–Kansas City. · Department of Computer Science, School of Science and Engineering, University of Missouri–Kansas City.
Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning
Feb 26, 2026Qiannian Zhao, Chen Yang, Jinhao Jing +5RL for Language Model Reasoning Large Reasoning Models
Algorithmic Blindness in Large Language Models: A Calibration Study of Performance Prediction
Feb 25, 2026Sohan Venkatesh, Ashish Mahendran Kurapath, Tejas MelkoteAlgorithmic Reasoning LLM Evaluation
Manipal Institute of Technology Bengaluru
How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding
Feb 23, 2026Wei Chen, Guoyang Ju, Yuanyuan QiLLM Input Optimization LLM Prompting
China Jiliang University, Hangzhou, China
UniRank: A Multi-Agent Calibration Pipeline for Estimating University Rankings from Anonymized Bibliometric Signals
Feb 21, 2026Pedram Riyazimehr, Seyyed Ehsan MahmoudiHigher Education Language Model Calibration
NotionWave
Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning
Feb 19, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaBayesian LoRA Parameter-Efficient Fine-Tuning
Department of Computer Science, Iowa State University · Department of Civil, Construction and Environmental Engineering, Iowa State University
Who can we trust? LLM-as-a-jury for Comparative Assessment
Feb 18, 2026Mengjie Qian, Guangzhi Sun, Mark J. F. Gales +1LLM-as-a-Judge Language Model Generation Evaluation
Department of Engineering, University of Cambridge, UK.
Know When You're Wrong: Aligning Confidence with Correctness for LLM Error Detection
Feb 18, 2026Xie Xiaohu, Liu Xiaohu, Yao BenjaminLanguage Model Error Detection Confidence Estimation in Language Models
Alexa AI, Amazon, Seattle, WA, USA
The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling
Feb 16, 2026Pierre-Alexandre Mattei, Bruno LoureiroModel Calibration LLM Uncertainty Estimation
Université Côte d’Azur, Inria, CNRS, LJAD, France · Département d’Informatique, École Normale Supérieure - PSL, CNRS, France
On Calibration of Large Language Models: From Response To Capability
Feb 14, 2026Sin-Han Yang, Cheng-Kuang Wu, Chieh-Yen Lin +3LLM Evaluation Confidence Estimation in Language Models
Appier AI Research · National Taiwan University
When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents
Feb 12, 2026Aman MehtaAI Agent Reliability Selective Prediction
Snowflake AI Research
Scalable Delphi: Large Language Models for Structured Risk Estimation
Feb 9, 2026Tobias Lorenz, Mario FritzLanguage Model Calibration
CISPA Helmholtz Center for Information Security, Germany
Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
Jan 28, 2026Moule Lin, Shuhao Guan, Andrea Patane +2Bayesian Neural Networks Confidence Estimation in Language Models
School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland and Lero the Research Ireland Centre for Software, Ireland · School of Computer Science, University College Dublin, Dublin, Ireland
Self-Aware Knowledge Probing: Evaluating Language Models' Relational Knowledge through Confidence Calibration
Jan 26, 2026Christopher Kissling, Elena Merdjanovska, Alan AkbikLanguage Model Probing Language Model Calibration
Humboldt-Universität zu Berlin · Science of Intelligence
NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems
Jan 16, 2026Jiayu Liu, Rui Wang, Qing Zong +9Retrieval-Augmented Generation Confidence Estimation in Language Models
HKUST · UIUC · Northwestern University
Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations
Jan 12, 2026Yuxi Xia, Dennis Ulmer, Terra Blevins +3LLM Uncertainty Estimation Confidence Estimation in Language Models
Faculty of Computer Science, UniVie Doctoral School Computer Science · Faculty of Philological and Cultural Studies, University of Vienna, Austria · ILLC, University of Amsterdam, Netherlands +2
Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Oct 3, 2025Ej Zhou, Caiqi Zhang, Tiancheng Hu +4Multilingual Language Models Multilingual Language Model Evaluation
Language Technology Lab, University of Cambridge
Post-Training Large Language Models via Reinforcement Learning from Self-Feedback
Jul 29, 2025Carel van Niekerk, Renato Vukovic, Benjamin Ruppik +3RL for Language Models RL for Language Model Reasoning
Heinrich Heine University Düsseldorf, Germany

Mar 6, 2026J/K move · Enter open · S save

From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty

Azza Jenane, Nassim Walha, Lukas Kuhn, Florian Buettner

German Cancer Research Center (DKFZ) German Cancer Consortium (DKTK) Goethe University Frankfurt, Germany

LLM Uncertainty Estimation Language Model Calibration

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Large Language Models (LLMs) that can express interpretable and calibrated uncertainty are crucial in high-stakes domains. While methods to compute uncertainty post-hoc exist, they are often sampling-based and therefore computationally expensive or lack calibration. We propose a three-stage pipeline to post-train LLMs to efficiently infer calibrated uncertainty estimates for their responses. First, we compute fine-grained entropy-based uncertainty scores on the training data, capturing the distributional variability of model outputs in embedding space. Second, these scores are calibrated via Platt scaling, producing reliable and human-interpretable uncertainty signals. Finally, the target LLM is post-trained via reinforcement learning to align its policy with these calibrated signals through a verifiable reward function. Unlike post-hoc uncertainty estimation methods, our approach provides interpretable and computationally efficient uncertainty estimates at test time. Experiments show that models trained with our pipeline achieve better calibration than baselines and generalize to unseen tasks without further processing, suggesting that they learn a robust uncertainty reasoning behavior.