Confidence Estimation in Language Models

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The field studies whether language models can tell when their answers are likely to be wrong, which matters for deciding when to answer, abstain, retrieve evidence, or spend more computation. Methods include verbalized confidence, token probabilities and sampling-based agreement, as well as hidden-state probes, activation signals, post-hoc calibration, and conformal prediction. These approaches are tested across settings such as factual question answering, mathematical reasoning, code generation, multilingual tasks, and tool-using agents. A central challenge is that confidence signals can disagree or shift with prompts, training, and distribution changes; good calibration or error ranking alone does not guarantee useful decisions or reliable risk guarantees.

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249 papers45 papers in the last four weeks, up 96% on the four weeks before. 0.3% of all new papers.

Semantic Self-Distillation for Language Model Uncertainty
Feb 4, 2026Edward Phillips, Sean Wu, Fredrik K. Gustafsson +2Uncertainty Quantification Confidence Estimation in Language Models
Department of Engineering Science, University of Oxford · Oxford Suzhou Centre for Advanced Research, University of Oxford, Suzhou
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
When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
Jan 12, 2026Chenjie Hao, Weyl Lu, Yuko Ishiwaka +3Adaptive Inference Confidence Estimation in Language Models
University of California, Davis · SoftBank Corp. · Aizip
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
PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains
Aug 29, 2025Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema +7Confidence Estimation in Language Models Best-of-N Sampling
Imperial College London · University of Edinburgh · UCL
Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity
May 23, 2025Shiyu Ni, Keping Bi, Jiafeng Guo +1LLM Uncertainty Estimation LLM Hallucination Mitigation
CAS Key Lab of Network Data Science and Technology, ICT, CAS · State Key Laboratory of AI Safety · University of Chinese Academy of Sciences

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Semantic Self-Distillation for Language Model Uncertainty

Edward Phillips, Sean Wu, Fredrik K. Gustafsson, Boyan Gao+1

Department of Engineering Science, University of Oxford · Oxford Suzhou Centre for Advanced Research, University of Oxford, Suzhou

Uncertainty Quantification Confidence Estimation in Language Models LLM Reliability Language Model Distillation LLM Uncertainty Estimation Semantic Uncertainty Estimation

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Large language models present challenges for principled uncertainty quantification, in part due to their complexity and the diversity of their outputs. Semantic dispersion, or the variance in the meaning of sampled answers, has been proposed as a useful proxy for model uncertainty, but the associated computational cost prohibits its use in latency-critical applications. We show that sampled semantic distributions can be distilled into lightweight student models which estimate a prompt-conditioned density before the language model generates an answer token. The student model predicts a semantic distribution over possible answers; the entropy of this distribution provides a prompt-level uncertainty signal, and the probability density allows answer-level reliability evaluation. Across experiments on TriviaQA and MMLU, we find our student models perform competitively relative to the teacher's sampled semantic dispersion on a hallucination prediction task, whilst offering additional uncertainty primitives for out-of-domain detection and multiple-choice answer selection. We term this technique Semantic Self-Distillation (SSD), which can serve as a general framework for distilling predictive uncertainty in complex output spaces beyond language.