Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

TL;DR

This paper proposes semantic entropy, a novel uncertainty measure for language models that accounts for linguistic invariances, improving the prediction of model accuracy in question answering tasks.

Contribution

It introduces semantic entropy, an unsupervised uncertainty metric that captures semantic equivalences without modifying existing language models.

Findings

Semantic entropy outperforms baseline uncertainty measures in predicting accuracy.

The method is unsupervised and requires no model modifications.

Semantic invariances improve uncertainty estimation in NLP tasks.

Abstract

We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence" -- different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy -- an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to off-the-shelf language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines.