How Do Uncertainty Estimators Align with Hallucination Occurrence in LLMs?
cs.CL, cs.AI, cs.LG, stat.ML
Submitted: 2026-05-26
Updated: 2026-10-12
Comments: 50 pages, 11 figures, 18 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
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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