Confidence Estimation in Language Models

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24 papers in the last four weeks, up 500% on the four weeks before. 0.2% of all new papers.

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Characterizing Overconfident Failure in LLM-Based Code Generation
Oct 8, 2026Ravishka Rathnasuriya, Wei YangLLM Code Generation Confidence Estimation in Language Models
Institute of Systems for Advanced Computing, Fudan University, Shanghai, China
The Confidence Game: Strategic Miscalibration in Human-AI Delegation
Oct 7, 2026Raghu Arghal, Saswati Sarkar, Shirin Saeedi BidokhtiLLM Agent Reliability Confidence Estimation in Language Models
Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia, PA, USA
U-Space: Uncovering When and Why Uncertainty Arises in Language Models
Oct 6, 2026Tobias Braun, Nils Loose, Alexander Herzog +4LLM Uncertainty Estimation Confidence Estimation in Language Models
Technische Universität Darmstadt · University College London · Universität zu Lübeck +2
Confidence Reasoning Graphs: Structured Confidence Estimation for LLM Agents
Oct 6, 2026Brendan King, Farima Fatahi Bayat, Jean-Flavien Bussotti +2Probability Calibration Confidence Estimation in Language Models
University of California, Santa Cruz · Megagon Labs
CLM-as-a-Judge: Evaluating an Open Contrastive Decision Model on Public Judge Benchmarks
Oct 5, 2026Gowthamkumar NandakishoreContrastive Learning LLM Evaluation
Independent researcher
ufakzeka-karar: An Open Turkish Typed-Decision Model with Order-Invariant Option Scoring
Oct 5, 2026Sait Furkan TekeLLM Evaluation Confidence Estimation in Language Models
ufak AI
Cross-lingual Calibration of Pre-Generation Success Probes for Multilingual LLM Routing
Oct 5, 2026Andrea Paganelli, Stefano Civelli, Pietro Bernardelle +1Confidence Estimation in Language Models Language Model Calibration
The University of Queensland · Polytechnic University of Milan
Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models
Oct 4, 2026Zhejun Jiang, Mijung ParkConfidence Estimation in Language Models Bayesian Inference
The University of British Columbia
Verbalized and Internal Probabilities Are Coupled in Large Language Models
Sep 30, 2026Sinead Williamson, Jiaxuan Li, Nick Foti +2Confidence Estimation in Language Models LLM Uncertainty Estimation
Apple
Also Small Models Can Reasonably Self-Evaluate Their Confidence
Sep 30, 2026Idil Kapikiran, Thomas Decker, Thomas RunklerConfidence Estimation in Language Models Small Language Models
Technical University of Munich · Siemens AG · LMU Munich +1
Benchmarking System One decision models against trained classifiers and language models for automated decision gates
Sep 29, 2026Amir Rafe, Subasish DasLLM Decision-Making LLM Evaluation
Texas State University, 601 University Drive, San Marcos, 78666, Texas, USA
Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs
Sep 29, 2026Feiyang Li, Shengjing Liu, Qi Zhan +7Uncertainty Quantification Confidence Estimation in Language Models
College of Computer Science and Software Engineering, Shenzhen University · Tsinghua University · Behavioral and Spatial AI Lab, Peking University & Tongji University
TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration
Sep 28, 2026Yuebin Xu, Xuemei Peng, Junlan Chen +2Confidence Estimation in Language Models Language Model Calibration
The Hong Kong University of Science and Technology (Guangzhou)
When Confidence Rises Too Early: Detecting Shortcut Reasoning via Premature Answer Commitment
Sep 28, 2026Zhaohan Zhang, Junjie Liu, Chengzhengxu Li +6Faithfulness of Language Model Explanations Confidence Estimation in Language Models
Queen Mary University of London · Tongyi Lab, Alibaba Group · Xi’an Jiaotong University
Semantic Uncertainty Quantification Needs Factual Equivalence
Sep 28, 2026Joseph Hoche, Quentin Guimard, Gianni FranchiConfidence Estimation in Language Models Factual Consistency Evaluation
AMIAD, Pˆole Recherche Palaiseau, France
On the Limits of Metacognitive Monitoring in LLMs
Sep 28, 2026Dongqi Han, Yifan Yang, Dongsheng LiLanguage Model Error Detection Confidence Estimation in Language Models
Microsoft Research Asia
Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts
Sep 28, 2026Chenxiao Fan, Chongming Gao, Gangyi Zhang +8Confidence Estimation in Language Models RL for Language Model Reasoning
University of Science and Technology of China · Qwen Business Unit of Alibaba · National University of Singapore
When Words Fall Short: Iterative Synergy Between Verbalized Reasoning and Hidden Features for LLM Confidence Estimation
Sep 28, 2026Yekun Xu, Ante Wang, Jingyi Ren +3Confidence Estimation in Language Models LLM Reliability
College of AI, Tsinghua University, Beijing, China · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China · Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China
LLMs learn different forms of metacognition when trained to predict their own accuracy
Sep 27, 2026Nicolas Yax, Stefano Palminteri, Pierre-Yves OudeyerConfidence Estimation in Language Models Language Model Calibration
LNC2, INSERM, Paris, France · DEC, ENS, PSL, Paris, France · Flowers AI & CogSci Lab +1
Shared Experience, Separate Learning: Companion Confidence Calibration for LLMs
Sep 27, 2026Shiyu Ni, Keping Bi, Jiafeng Guo +4Confidence Estimation in Language Models Language Model Calibration
State Key Laboratory of AI Safety · Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences
Calibration, Not Answer Selection: Distilling Internal Confidence in Reasoning Models
Sep 27, 2026Yadong Xi, Rongsheng Zhang, Tangjie Lv +2Confidence Estimation in Language Models Language Model Calibration
NetEase · Amazon · Singapore University of Technology and Design
Broadening Uncertainty Estimation for Audio Question Answering Across Methods, Formats, and Inputs
Sep 24, 2026Aaron Isidore Grace, Weiran WangAudio-Language Model Evaluation Audio QA
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada · Department of Computer Science, University of Iowa, Iowa City, IA, USA
Calibration as a First-Class Criterion in LLM Evaluation
Sep 22, 2026Mario Sanz-Guerrero, Katharina von der WenseLLM Evaluation Confidence Estimation in Language Models
Johannes Gutenberg University Mainz, Germany · University of Colorado Boulder, USA
Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models
Sep 21, 2026Kevin David Hayes, Arka Pal, Haosong Zhang +2Language Model Error Detection Confidence Estimation in Language Models
Department of Computer Science, University of Maryland · Ritual AI · Fudan University +1
An Analysis of Training-Free Self-Reported Confidence in Language Models
Sep 17, 2026Lukas Meyer, Sofia Rossi, Wei Chen +2Confidence Estimation in Language Models Language Model Self-Assessment
DreamAI
Intrinsic Sequence-Likelihood Confidence in Retrieval-Dominated Extractive QA: Two Pre-Specified Negatives, and What They Do and Do Not Attribute
Sep 17, 2026Gunwoo Lee, Changmin Sung, Sang-Hwan Gwak +3LLM Evaluation Selective Prediction
Large-scale AI Research Center, Korea Institute of Science and Technology Information (KISTI), 245 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. · Department of Applied AI, University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea.
The Mirage of Calibrated Confidence: Trajectory-Independence of Verbalized Confidence in Vision-Language Models
Sep 16, 2026Jisoo Yang, Jaeho Han, Trung X. Pham +1VLM Evaluation Confidence Estimation in Language Models
Department of Artificial Intelligence, Chung-Ang University, Republic of Korea · Van Lang University, Ho Chi Minh City, Vietnam
Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents
Sep 15, 2026Caiqi Zhang, Xiaochen Zhu, Chengzu Li +3Confidence Estimation in Language Models LLM Uncertainty Estimation
University of Cambridge 1 Solve & Estimate · Google DeepMind
When Confidence Signals Disagree: Local and Global Confidence in Autoregressive Language Models
Sep 15, 2026Julio C. Amador Diaz LopezLLM Evaluation Confidence Estimation in Language Models
SiftyML, LTD London, United Kingdom
Rethinking Verbalized Confidence for LLM-as-a-Judge: A Compatibility Shift on Post-2025 Proprietary Models
Sep 11, 2026Yu-Chung HsiaoLLM Evaluation LLM-as-a-Judge
Cisco Systems

Oct 8, 2026, cs.SEJ/K move · Enter open · S save

Characterizing Overconfident Failure in LLM-Based Code Generation

Ravishka Rathnasuriya, Wei Yang

Institute of Systems for Advanced Computing, Fudan University, Shanghai, China

LLM Code Generation Confidence Estimation in Language Models LLM Uncertainty Estimation Code Generation Evaluation

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Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while still failing execution-based correctness checks. Existing validation methods, such as testing and program analysis, remain essential but are often incomplete, costly, or applied only after generation. Model-derived uncertainty is therefore a natural early reliability signal. This paper studies the dilemma of overconfidence in code LLMs where incorrect programs are often generated with token-level confidence comparable to correct programs. We study this dilemma across four open-source code models and three execution-based benchmarks. Our analysis begins by investigating whether existing uncertainty metrics provide reliable proxies for execution correctness in code generation. We then characterize overconfidence at both global and local token levels, asking whether incorrect programs remain indistinguishable from correct ones under confidence and entropy summaries, including selective generation and the limits of instruction tuning. Finally, we evaluate whether common mitigation strategies reduce this failure mode. Our study yields four findings. First, existing uncertainty signals provide only partial and model-dependent evidence of execution failure. Second, overconfidence persists at both program and token levels, and uncertainty-based selection does not consistently improve accepted-set accuracy. Third, instruction tuning can increase certainty on failing generations without consistently improving correctness discrimination. Fourth, common mitigation techniques improve specific aspects of reliability but do not reliably resolve overconfident failure. Our exploratory latent analysis suggests that hidden representations may encode correctness-related signals that output confidence does not expose.

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