Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

TL;DR

This paper introduces a semantic embedding-based method for more accurate and robust uncertainty quantification in large language models, reducing sensitivity to wording differences and computational costs.

Contribution

It presents a novel semantic embedding approach and an amortised model for efficient uncertainty estimation in LLMs, outperforming traditional likelihood-based methods.

Findings

More accurate uncertainty estimates across multiple datasets

Reduced overestimation of uncertainty due to irrelevant words

Lower computational overhead with the amortised approach

Abstract

Accurately quantifying uncertainty in large language models (LLMs) is crucial for their reliable deployment, especially in high-stakes applications. Current state-of-the-art methods for measuring semantic uncertainty in LLMs rely on strict bidirectional entailment criteria between multiple generated responses and also depend on sequence likelihoods. While effective, these approaches often overestimate uncertainty due to their sensitivity to minor wording differences, additional correct information, and non-important words in the sequence. We propose a novel approach that leverages semantic embeddings to achieve smoother and more robust estimation of semantic uncertainty in LLMs. By capturing semantic similarities without depending on sequence likelihoods, our method inherently reduces any biases introduced by irrelevant words in the answers. Furthermore, we introduce an amortised version of our approach by explicitly modelling semantics as latent variables in a joint probabilistic model. This allows for uncertainty estimation in the embedding space with a single forward pass, significantly reducing computational overhead compared to existing multi-pass methods. Experiments across multiple question-answering datasets and frontier LLMs demonstrate that our embedding-based methods provide more accurate and nuanced uncertainty quantification than traditional approaches.