Hallucination, i.e., generating factually incorrect content, remains a critical challenge for large language models (LLMs). We introduce TOHA, a TOpology-based HAllucination detector in the RAG setting, which leverages a topological divergence metric to quantify the structural properties of graphs induced by attention matrices. Examining the topological divergence between prompt and response subgraphs reveals consistent patterns: higher divergence values in specific attention heads correlate with hallucinated outputs, independent of the dataset. Extensive experiments, including evaluation on question answering and data-to-text tasks, show that our approach achieves state-of-the-art or competitive results on several benchmarks, two of which were annotated by us and are being publicly released to facilitate further research. Beyond its strong in-domain performance, TOHA maintains remarkable domain transferability across multiple open-source LLMs. Our findings suggest that analyzing the topological structure of attention matrices can serve as an efficient and robust indicator of factual reliability in LLMs.

本研究针对大型语言模型中的幻觉，即生成事实不准确内容的问题，提出了TOHA，这是一种基于拓扑的方法来检测幻想。通过分析提示和响应子图的拓扑差异，研究发现特定注意力头的差异值与幻想输出存在一致的关联。研究结果表明，拓扑结构分析为大型语言模型的事实可靠性提供了一种有效的指示方法。

通过注意力图的拓扑差异检测大型语言模型中的幻想