对谎言说谎：知识图谱在大语言模型鲁棒性幻觉自检测中的应用
Lie to Me: Knowledge Graphs for Robust Hallucination Self-Detection in LLMs
AI总结：
本研究提出利用知识图谱提升大语言模型幻觉自检的鲁棒性，通过转换响应为图谱并估算幻觉可能性，实现准确率和F1分数的显著提升。
摘要：
幻觉，即生成看似可信但虚假的陈述，仍然是安全部署大语言模型（LLMs）的主要障碍。基于自我检测方法的强大表现，我们探讨了使用结构化知识表示，即知识图谱，来提高幻觉自我检测的效果。具体而言，我们提出了一种简单而有力的方法，通过将LLM响应转换为实体和关系的知识图谱，并利用这些图谱来估计响应中包含幻觉的可能性。我们使用两种广泛使用的LLM，GPT-4o和Gemini-2.5-Flash，在两个幻觉检测数据集中评估了所提出的方法。为了支持更可靠的未来基准测试，其中一个数据集已手动整理和增强，并作为本研究的次要成果发布。与标准自我检测方法和SelfCheckGPT等最先进的方法相比，我们的方法在准确率上提高了高达16%，F1分数提高了20%。我们的结果表明，当LLM将事实结构化为知识图谱时，能够更有效地分析基本事实，即使初始输出存在不准确性。这种低成本、模型无关的方法为更安全、更可信赖的语言模型铺平了道路。
英文摘要：
Hallucinations, the generation of apparently convincing yet false statements, remain a major barrier to the safe deployment of LLMs. Building on the strong performance of self-detection methods, we examine the use of structured knowledge representations, namely knowledge graphs, to improve hallucination self-detection. Specifically, we propose a simple yet powerful approach that enriches hallucination self-detection by (i) converting LLM responses into knowledge graphs of entities and relations, and (ii) using these graphs to estimate the likelihood that a response contains hallucinations. We evaluate the proposed approach using two widely used LLMs, GPT-4o and Gemini-2.5-Flash, across two hallucination detection datasets. To support more reliable future benchmarking, one of these datasets has been manually curated and enhanced and is released as a secondary outcome of this work. Compared to standard self-detection methods and SelfCheckGPT, a state-of-the-art approach, our method achieves up to 16% relative improvement in accuracy and 20% in F1-score. Our results show that LLMs can better analyse atomic facts when they are structured as knowledge graphs, even when initial outputs contain inaccuracies. This low-cost, model-agnostic approach paves the way toward safer and more trustworthy language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
