How Do Uncertainty Estimators Align with Hallucination Occurrence in LLMs?

arXiv:2605.27016 · cs.CL, cs.AI, cs.LG, stat.ML · Submitted 2026-05-26 · Read on arXiv

cs.CL, cs.AI, cs.LG, stat.ML

Submitted: 2026-05-26

Updated: 2026-10-12

Terminology

• Hallucination — 9× in this paper · explained in 4 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes

Sources

• International AI Safety Report
• From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered
• HalluHard: A Hard Multi-Turn Hallucination Benchmark
• Language Models (Mostly) Know What They Know
• Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
• A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
• The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguity
• Measuring short-form factuality in large language models
• UncertaintyZoo: A Unified Toolkit for Quantifying Predictive Uncertainty in Deep Learning Systems
• Uncertainty-aware Language Modeling for Selective Question Answering
• AlignScore: Evaluating Factual Consistency with a Unified Alignment Function
• Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads
• A Survey of Large Language Models

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