Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review

2025-04-25

AI Summary

This study addresses the “hallucination” problem in large language models (LLMs) arising from inadequate uncertainty quantification (UQ) and poor calibration. We present the first systematic survey of LLM uncertainty calibration methods and introduce the first comprehensive UQ and calibration benchmark specifically designed for LLMs. Our standardized empirical evaluation covers six major calibration approaches across two reliability-focused datasets. We propose a unified evaluation framework incorporating confidence-accuracy alignment analysis, Expected Calibration Error (ECE), and Brier Score. Results reveal that existing methods achieve only limited calibration performance; furthermore, task type, prompt engineering, and output format significantly influence uncertainty estimation quality. To foster reproducible research, we open-source our evaluation protocol and analytical toolchain. This work establishes a rigorous, community-accessible benchmark and methodological foundation for advancing LLM reliability and trustworthy AI.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) have been transformative across many domains. However, hallucination -- confidently outputting incorrect information -- remains one of the leading challenges for LLMs. This raises the question of how to accurately assess and quantify the uncertainty of LLMs. Extensive literature on traditional models has explored Uncertainty Quantification (UQ) to measure uncertainty and employed calibration techniques to address the misalignment between uncertainty and accuracy. While some of these methods have been adapted for LLMs, the literature lacks an in-depth analysis of their effectiveness and does not offer a comprehensive benchmark to enable insightful comparison among existing solutions. In this work, we fill this gap via a systematic survey of representative prior works on UQ and calibration for LLMs and introduce a rigorous benchmark. Using two widely used reliability datasets, we empirically evaluate six related methods, which justify the significant findings of our review. Finally, we provide outlooks for key future directions and outline open challenges. To the best of our knowledge, this survey is the first dedicated study to review the calibration methods and relevant metrics for LLMs.

Problem

• Assessing and quantifying uncertainty in Large Language Models
• Evaluating effectiveness of uncertainty measurement methods for LLMs
• Providing a benchmark for comparing calibration techniques in LLMs

Innovation

• Systematic survey of UQ and calibration methods
• Rigorous benchmark for comparing existing solutions
• Empirical evaluation using reliability datasets