Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions

TL;DR

The paper tackles hallucinations in large language models by reframing reliability as a quantifiable uncertainty problem. It develops a four axis taxonomy of UQ methods and surveys token level, output level, internal state, and self checking approaches, while also discussing model accessibility and training reliance. Empirical results across short and long form QA on multiple models reveal strengths and tradeoffs among methods, underscoring practical considerations for deployment. The authors discuss limitations such as knowledge update gaps and interpretability and propose directions including long form claim level evaluation and theoretically grounded UQ to improve robustness and applicability in real world settings.

Abstract

The rapid advancement of large language models (LLMs) has transformed the landscape of natural language processing, enabling breakthroughs across a wide range of areas including question answering, machine translation, and text summarization. Yet, their deployment in real-world applications has raised concerns over reliability and trustworthiness, as LLMs remain prone to hallucinations that produce plausible but factually incorrect outputs. Uncertainty quantification (UQ) has emerged as a central research direction to address this issue, offering principled measures for assessing the trustworthiness of model generations. We begin by introducing the foundations of UQ, from its formal definition to the traditional distinction between epistemic and aleatoric uncertainty, and then highlight how these concepts have been adapted to the context of LLMs. Building on this, we examine the role of UQ in hallucination detection, where quantifying uncertainty provides a mechanism for identifying unreliable generations and improving reliability. We systematically categorize a wide spectrum of existing methods along multiple dimensions and present empirical results for several representative approaches. Finally, we discuss current limitations and outline promising future research directions, providing a clearer picture of the current landscape of LLM UQ for hallucination detection.

Figures (3)

• Figure 1: Overview of this survey. We discuss the background of UQ, categorization and detailed description of existing methods, evaluation, limitations and future directions.
• Figure 2: Examples of aleatoric and epistemic uncertainty in natural language.
• Figure 3: Categorization of uncertainty quantification methods based on conceptual approaches.