← Back to Catalog

Uncertainty Estimation

A family of methods for estimating or quantifying how uncertain a model's output is, using information such as probability distributions, differences across repeated outputs, semantic variation, or ensembles combining multiple outputs or methods.

Aliases: Uncertainty Quantification; UQ.

Example

Repeated answers to the same question sometimes converge on nearly the same response and sometimes diverge substantially. That difference is used as a signal of uncertainty.

Distinctions and cautions

Confidence is a score or expression attached to a particular prediction; uncertainty estimation is a method or process for obtaining information about uncertainty. Whether estimated uncertainty corresponds appropriately to the actual likelihood of error is a calibration question.

Open questions and cautions

Different methods measure different types of uncertainty. It has not been established to what extent their values can be compared directly across free-form responses, long-form text, multilingual settings, and agentic use.

Evidence

View evidence →

Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models

Title
Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models
Author / organization
Tonolini et al.
Year
2024
Source type / publication status
Findings ACL peer-reviewed research; published
Supported concepts / claim context
Uncertainty Estimation; prompt uncertainty.
Scope and limitations
Mainly natural-language classification; uses a small labeled validation set.
Access / version note
Published ACL Findings.
Recorded official URL / DOI / identifier
DOI: 10.18653/v1/2024.findings-acl.728 (DOI resolver)

Detecting hallucinations in large language models using semantic entropy

Title
Detecting hallucinations in large language models using semantic entropy
Author / organization
Farquhar et al.
Year
2024
Source type / publication status
Nature peer-reviewed research; published
Supported concepts / claim context
Uncertainty Estimation, Abstention, Hallucination.
Scope and limitations
Requires sampling and clustering by semantic equivalence; cost and applicability depend on the setting.
Access / version note
Published Nature article.
Recorded official URL / DOI / identifier
DOI: 10.1038/s41586-024-07421-0 (DOI resolver)