Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses

TL;DR

This paper presents a multi-dimensional uncertainty quantification framework for large language models that combines semantic and knowledge-based analysis to improve response reliability, especially in critical applications.

Contribution

It introduces a novel multi-dimensional UQ method that integrates semantic and knowledge-aware similarity analysis using tensor decomposition, advancing beyond existing semantic-only approaches.

Findings

Outperforms existing UQ methods in identifying uncertain responses

Captures both semantic variations and factual inconsistencies

Provides a more robust uncertainty representation for high-stakes use cases

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks due to large training datasets and powerful transformer architecture. However, the reliability of responses from LLMs remains a question. Uncertainty quantification (UQ) of LLMs is crucial for ensuring their reliability, especially in areas such as healthcare, finance, and decision-making. Existing UQ methods primarily focus on semantic similarity, overlooking the deeper knowledge dimensions embedded in responses. We introduce a multi-dimensional UQ framework that integrates semantic and knowledge-aware similarity analysis. By generating multiple responses and leveraging auxiliary LLMs to extract implicit knowledge, we construct separate similarity matrices and apply tensor decomposition to derive a comprehensive uncertainty representation. This approach disentangles overlapping information from both semantic and knowledge dimensions, capturing both semantic variations and factual consistency, leading to more accurate UQ. Our empirical evaluations demonstrate that our method outperforms existing techniques in identifying uncertain responses, offering a more robust framework for enhancing LLM reliability in high-stakes applications.