大语言模型中幻觉检测的不确定性量化：基础、方法与未来方向
Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
AI总结：
本文系统综述了不确定性量化在大语言模型幻觉检测中的基础理论、方法分类与实证结果，并指出了当前局限与未来研究方向。
摘要：
大语言模型（LLM）的快速发展彻底改变了自然语言处理的格局，推动了包括问答、机器翻译和文本摘要等广泛领域的突破。然而，它们在现实世界应用中的部署引发了关于可靠性和可信度的担忧，因为LLM仍然容易产生幻觉，即生成看似合理但事实错误的输出。不确定性量化（UQ）已成为解决这一问题的重要研究方向，为评估模型生成的可信度提供了原则性的度量方法。我们首先介绍UQ的基础，从其正式定义到认知不确定性与偶然不确定性之间的传统区分，然后重点阐述这些概念如何被适应到LLM的语境中。在此基础上，我们考察了UQ在幻觉检测中的作用，其中量化不确定性提供了一种识别不可靠生成并提高可靠性的机制。我们沿着多个维度系统地分类了广泛的现有方法，并展示了若干代表性方法的实证结果。最后，我们讨论了当前的局限性并概述了有前景的未来研究方向，为LLM在幻觉检测中的UQ现状提供了更清晰的图景。
英文摘要：
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.
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