Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses

2025-02-24

AI Summary

To address the insufficient uncertainty quantification (UQ) of large language models (LLMs) in high-stakes domains such as healthcare and finance, this paper proposes the first knowledge-aware, multidimensional response UQ framework. Unlike existing methods relying solely on semantic similarity, our approach constructs a dual-channel similarity matrix via multi-response sampling and auxiliary LLM-based knowledge distillation. We further introduce CP tensor decomposition to explicitly decouple semantic variability from factual consistency—enabling fine-grained, interpretable uncertainty representation. Evaluated across multiple benchmark tasks, our method achieves an average 12.7% improvement in uncertain response identification accuracy. This advancement significantly enhances the reliability and trustworthiness of LLM deployments in safety-critical applications.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Quantify uncertainty in Large Language Models
• Integrate semantic and knowledge-aware similarity analysis
• Enhance reliability of LLMs in high-stakes applications

Innovation

• Multi-dimensional UQ framework integration
• Tensor decomposition for uncertainty representation
• Auxiliary LLMs for knowledge extraction