通过语义层面内部推理图检测检索增强生成中的忠实性幻觉
Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph
AI总结：
本文提出基于语义层面内部推理图的方法，用于检测检索增强生成中的忠实性幻觉，通过构建依赖关系图和动态阈值调整提升检测性能。
摘要：
基于大型语言模型（LLM）的检索增强生成（RAG）系统已取得显著进展。它可以有效减少事实性幻觉，但忠实性幻觉仍存在。先前检测忠实性幻觉的方法要么忽视了模型内部推理过程，要么处理这些特征过于粗糙，使判别器难以学习。本文提出了一种基于语义层面内部推理图的方法来检测忠实性幻觉。具体而言，我们首先将分层相关性传播算法从词层面扩展到语义层面，构建基于归因向量的内部推理图。这提供了更忠实的语义层面依赖表示。此外，我们设计了一个基于小预训练语言模型的通用框架，利用LLM推理中的依赖性进行训练和幻觉检测，可以通过阈值动态调整正确样本的通过率。实验结果表明，我们的方法在RAGTruth和Dolly-15k上比最先进的基线方法整体性能更好。
英文摘要：
The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist. Previous methods for detecting faithfulness hallucinations either neglect to capture the models' internal reasoning processes or handle those features coarsely, making it difficult for discriminators to learn. This paper proposes a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination. Specifically, we first extend the layer-wise relevance propagation algorithm from the token level to the semantic level, constructing an internal reasoning graph based on attribution vectors. This provides a more faithful semantic-level representation of dependency. Furthermore, we design a general framework based on a small pre-trained language model to utilize the dependencies in LLM's reasoning for training and hallucination detection, which can dynamically adjust the pass rate of correct samples through a threshold. Experimental results demonstrate that our method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k.
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。
