Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
This addresses reliability issues for users deploying LLMs in real-world applications, but it is incremental as it reviews and categorizes existing methods rather than introducing new ones.
The paper tackles the problem of hallucinations in large language models (LLMs) by exploring uncertainty quantification (UQ) as a method for detecting unreliable outputs, providing a systematic review and empirical results of existing approaches.
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.