LLM Reliability

LLM: Large Language Model

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78 papers in the last four weeks, up 105% on the four weeks before. 0.8% of all new papers.

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Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Prediction
May 4, 2026Florian Geissler, Francesco Carella, Laura Fieback +1Retrieval-Augmented Generation Factual Consistency Evaluation
Fraunhofer Institute for Cognitive Systems (IKS), Munich, Germany · Volkswagen AG, Wolfsburg, Germany
HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs
May 4, 2026Ahmed CherifLLM Evaluation LLM Hallucination Mitigation
1Sofrecom Tunisia, Orange Innovation, Tunis, 1053, Tunisia.
Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training
May 4, 2026Luong N. NguyenConfidence Estimation in Language Models Language Model Self-Assessment
What Single-Prompt Accuracy Misses: A Multi-Variant Reliability Audit of Language Models
May 3, 2026Ranit Karmakar, Jayita ChatterjeeLLM Evaluation Language Model Calibration
Harvard University, Boston, MA 02115 · Boston, MA 02134
Language models fail at extended rule following
May 3, 2026Tianxiang Dai, Jonathan FanLLM Evaluation LLM Reliability
Department of Electrical Engineering, Stanford University, Stanford, CA 94305.
Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
May 3, 2026Wen Luo, Guangyue Peng, Liang Wang +7Long-Form Document Generation LLM Hallucination Mitigation
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University Research Asia · MSRA · Microsoft
Hallucinations Undermine Trust; Metacognition is a Way Forward
May 2, 2026Gal Yona, Mor Geva, Yossi MatiasLLM Hallucination Mitigation Hallucination in Language Models
Google Research · Tel Aviv University
CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
May 1, 2026Kevin H. Guo, Chao Yan, Avinash Baidya +5Healthcare LLM Reliability
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA · Intuit AI Research, Mountain View, CA, USA · Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA +2
"I Don't Know" -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation
May 1, 2026Daan Di Scala, Maaike de Boer, Pınar YolumRetrieval-Augmented Generation LLM Reliability
TNO Netherlands Organisation for Applied Scientific Research, Department Data Science · Utrecht University, Department of Information and Computing Sciences
Models Recall What They Violate: Constraint Adherence in Multi-Turn LLM Ideation
Apr 30, 2026Garvin KruthofLLM Evaluation LLM Alignment
Technical University of Munich
Geometry-Calibrated Conformal Abstention for Language Models
Apr 30, 2026Rui Xu, Yi Chen, Sihong Xie +1Uncertainty Quantification Selective Prediction
Information Hub, AI Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China
Belief-Guided Inference Control for Large Language Model Services via Verifiable Observations
Apr 30, 2026Wenhao Yuan, Chenchen Lin, Jian Chen +3LLM Inference Cost-Aware Inference
The University of Hong Kong · Sun Yat-sen University
Know2Guess: A Contamination-Aware Multi-Zone Benchmark for Knowledge-Boundary Evaluation in Large Language Models
Apr 30, 2026Renwei Meng, Bowen Zhang, Jian Wang +4LLM Evaluation LLM Auditing
Anhui University, Hefei, China
MoRFI: Monotonic Sparse Autoencoder Feature Identification
Apr 29, 2026Dimitris Dimakopoulos, Shay B. Cohen, Ioannis KonstasLLM Hallucination Mitigation LLM Interpretability
University of Edinburgh, UK · Heriot-Watt University, UK
From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
Apr 29, 2026Md. Rejaul Korim Sadi, Toufiqur Rahman Tasin, Golam Mostofa NaeemHallucination in Language Models LLM Reliability
*Dept. of Computer Science and Engineering, Metropolitan University, Sylhet, Bangladesh
HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models
Apr 28, 2026Guoshenghui Zhao, Tan Yu, Weijie ZhaoHallucination Detection Hallucination in Language Models
Rochester Institute of Technology · NVIDIA Corporation
Large language models eroding science understanding: an empirical study of malignment
Apr 28, 2026Harry Collins, Hartmut Grote, Paul Newbury +2LLM Evaluation Knowledge Conflicts in Language Models
i · ii · iii
Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors
Apr 28, 2026Long Zhang, Zi-bo Qin, Wei-neng ChenKnowledge Conflicts in Language Models LLM Auditing
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
An Information-Geometric Framework for Stability Analysis of Large Language Models under Entropic Stress
Apr 27, 2026Hikmat Karimov, Rahid Zahid AlekberliLLM Evaluation LLM Reliability
Institute of Defense Technologies and Cybersecurity„ Azerbaijan Technical University, Azerbaijan
Distilling Self-Consistency into Verbal Confidence: A Pre-Registered Negative Result and Post-Hoc Rescue on Gemma 3 4B
Apr 27, 2026Jon-Paul CacioliSupervised Fine-Tuning Confidence Estimation in Language Models
Independent Researcher, Melbourne, Australia
Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning
Apr 27, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaContinual Learning for LLMs Conformal Prediction
Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University
Process Supervision of Confidence Margin for Calibrated LLM Reasoning
Apr 25, 2026Liaoyaqi Wang, Chunsheng Zuo, William Jurayj +2Confidence Estimation in Language Models RL for Language Model Reasoning
Johns Hopkins University
AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
Apr 24, 2026Pouya Pezeshkpour, Estevam HruschkaLLM Evaluation LLM Answer Verification
Megagon Labs
Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models
Apr 24, 2026Prakul Sunil Hiremath, Harshit R. HiremathConfidence Estimation in Language Models CoT Reasoning
Department of Computer Science and Engineering Visvesvaraya Technological University, Belagavi · Department of Computer Science and Business System SG Balekundri Institute of Technology, Belagavi
Verbal Confidence Saturation in 3-9B Open-Weight Instruction-Tuned LLMs: A Pre-Registered Psychometric Validity Screen
Apr 24, 2026Jon-Paul CacioliLLM Evaluation Confidence Estimation in Language Models
Independent Researcher, Melbourne, Australia
How Large Language Models Balance Internal Knowledge with User and Document Assertions
Apr 24, 2026Shuowei Li, Haoxin Li, Wenda Chu +1LLM Sycophancy Knowledge Augmentation for Language Models
Santa Clara University · Nanyang Technological University · California Institute of Technology
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
Apr 23, 2026Boyu Xiao, Xiuqi Tian, Xuwen Song +4LLM Sycophancy Healthcare
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China
Differentiable Conformal Training for LLM Reasoning Factuality
Apr 22, 2026Nathan Hittesdorf, Marco Salzetta, Lu ChengLLM Hallucination Mitigation Conformal Prediction
Department of Computer Science, University of Illinois at Chicago, Chicago, United States · Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
Are LLM Uncertainty and Correctness Encoded by the Same Features? A Functional Dissociation via Sparse Autoencoders
Apr 21, 2026Het Patel, Tiejin Chen, Hua Wei +2LLM Interpretability LLM Reliability
University of California, Riverside, CA · Arizona State University, Tempe, AZ
CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
Apr 21, 2026Vanessa Figueiredo, Wilter FranceschiAI Agent Reliability Language Model-Based Control
Orbital Sea Regina, SK, Canada

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Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Prediction

Florian Geissler, Francesco Carella, Laura Fieback, Jakob Spiegelberg

Fraunhofer Institute for Cognitive Systems (IKS), Munich, Germany · Volkswagen AG, Wolfsburg, Germany

Retrieval-Augmented Generation Factual Consistency Evaluation LLM Reliability RAG Evaluation

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Incorporating specific knowledge into large language models via retrieval-augmented generation (RAG) is a widespread technique that fuels many of today's industry AI applications. A fundamental problem is to assess if the context retrieved by some similarity search provides indeed supporting facts, or instead misguides the generator with irrelevant information. It is critical to associate meaningful confidence measures about the factuality of the retrieval process with the generated answers. We present a new, two-staged approach to predict fact faithfulness of the output of retrieval-augmented generations. First, we employ conformal prediction to select only those retrieved chunks who have a high chance to come from the correct source. This approach in itself can improve answer quality by up to 6% in some of the studied datasets, however, the associated statistical guarantees do not hold generally, since the assumption of sample exchangeability depends on the retriever setup. We present diagnostic metrics to assess whether a setup is suitable. Second, we quantify confidence in the consistency of a generated final answer with a given retrieved context, using an attention-based factuality classifier. This approach can detect inconsistent answers with a chance of up to 77%. Our work helps to establish a novel type of certified RAG systems for a broad range of natural language industry applications.

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