LLM Uncertainty Estimation

Large language model uncertainty estimation studies how to tell when an answer, reasoning step, or action may be unreliable, and whether the cause is missing knowledge, ambiguous input, or random variation. Methods include sampling and semantic agreement, token probabilities and entropy, confidence calibration, probes of hidden representations, and Bayesian or evidential models; conformal prediction adds statistical guarantees for abstention or prediction sets. These signals are used to decide when to answer, ask for clarification, retrieve evidence, spend more computation, or defer to a person, with tests spanning factual QA, reasoning, medicine, code, and multimodal tasks. A central challenge is that no signal works reliably across settings: confidence can be miscalibrated or reflect commitment rather than correctness, and agreement may preserve shared errors, so methods need task-specific validation and robust evaluation under ambiguity and distribution shift.