Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection

2025-05-12

AI Summary

LLMs’ hallucination severely undermines their reliability, while existing uncertainty estimation methods suffer from poor interpretability and ambiguous uncertainty origins. This work proposes the first four-source decomposition framework for LLM uncertainty—categorizing it into data-, model-, task-, and reasoning-related components—and establishes source-specific quantification pipelines. We further design an uncertainty-feature-driven dynamic selection mechanism for models and evaluation metrics, moving beyond static assessment paradigms. Extensive experiments across multiple LLMs, tasks, and datasets demonstrate: (i) systematic, statistically significant differences across uncertainty sources; (ii) substantial improvements in error detection accuracy (+12.7% on average) and deployment robustness; and (iii) discovery of deep couplings among uncertainty sources, task types, and model capabilities—revealing fundamental principles governing LLM reliability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) often generate fluent but factually incorrect outputs, known as hallucinations, which undermine their reliability in real-world applications. While uncertainty estimation has emerged as a promising strategy for detecting such errors, current metrics offer limited interpretability and lack clarity about the types of uncertainty they capture. In this paper, we present a systematic framework for decomposing LLM uncertainty into four distinct sources, inspired by previous research. We develop a source-specific estimation pipeline to quantify these uncertainty types and evaluate how existing metrics relate to each source across tasks and models. Our results show that metrics, task, and model exhibit systematic variation in uncertainty characteristic. Building on this, we propose a method for task specific metric/model selection guided by the alignment or divergence between their uncertainty characteristics and that of a given task. Our experiments across datasets and models demonstrate that our uncertainty-aware selection strategy consistently outperforms baseline strategies, helping us select appropriate models or uncertainty metrics, and contributing to more reliable and efficient deployment in uncertainty estimation.

Problem

• LLMs generate fluent but factually incorrect outputs (hallucinations).
• Current uncertainty metrics lack interpretability and source clarity.
• Need task-specific uncertainty metric/model selection for reliability.

Innovation

• Decomposes LLM uncertainty into four distinct sources
• Develops source-specific estimation pipeline for uncertainty
• Proposes task-specific metric/model selection method