Uncertainty Estimators Fail to Predict LLM Hallucinations
A recent investigation published on arXiv thoroughly examines how uncertainty estimation techniques correlate with hallucinations in large language models. This study distinguishes between intrinsic hallucinations, which involve breaches of input fidelity, and extrinsic hallucinations, characterized by unsubstantiated assertions. Various estimation methods, including information-theoretic, sampling-based, and reflexive approaches, were assessed in multiple contexts. The results question the widely held belief that uncertainty indicators consistently signal model shortcomings.
Key facts
- arXiv paper 2605.27016
- Study evaluates uncertainty estimators for LLM hallucination detection
- Covers intrinsic and extrinsic hallucinations
- Tests information-theoretic, sampling-based, and reflexive estimators
- Challenges assumption that uncertainty proxies indicate model failure
Entities
Institutions
- arXiv