自然语言生成中自动幻觉评估综述
A Survey of Automatic Hallucination Evaluation on Natural Language Generation
AI总结：
本文系统综述105种自然语言生成自动幻觉评估方法，构建数据集、基准与方法论分类框架，指出LLM时代的局限并提出未来研究路线图。
摘要：
大语言模型（LLM）的快速发展带来了一项紧迫挑战：如何可靠地评估幻觉，以保证模型的可信度。尽管自动幻觉评估（AHE）已成为这一工作中不可或缺的组成部分，但该领域在方法论上仍然分散，限制了概念清晰度和实践进展。本综述通过系统分析105种评估方法来解决这一关键空白，揭示其中77.1%专门针对LLM，这一范式转变要求新的评估框架。我们基于对基础性数据集和基准的综述，以及评估方法的分类体系，构建了一个结构化框架来组织该领域；这些内容共同系统记录了从前LLM到后LLM方法的演进。除分类组织之外，我们还指出了当前方法的根本局限及其对真实世界部署的影响。为指导未来研究，我们阐述了关键挑战并提出战略方向，包括增强可解释性机制以及整合面向特定应用的评估标准，最终为开发更稳健、更实用的幻觉评估系统提供路线图。
英文摘要：
The rapid advancement of Large Language Models (LLMs) has brought a pressing challenge: how to reliably assess hallucinations to guarantee model trustworthiness. Although Automatic Hallucination Evaluation (AHE) has become an indispensable component of this effort, the field remains fragmented in its methodologies, limiting both conceptual clarity and practical progress. This survey addresses this critical gap through a systematic analysis of 105 evaluation methods, revealing that 77.1% specifically target LLMs, a paradigm shift that demands new evaluation frameworks. We formulate a structured framework to organize the field, based on a survey of foundational datasets and benchmarks and a taxonomy of evaluation methodologies, which together systematically document the evolution from pre-LLM to post-LLM approaches. Beyond taxonomical organization, we identify fundamental limitations in current approaches and their implications for real-world deployment. To guide future research, we delineate key challenges and propose strategic directions, including enhanced interpretability mechanisms and integration of application-specific evaluation criteria, ultimately providing a roadmap for developing more robust and practical hallucination evaluation systems.
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