Large Language Models (LLMs) excel in fluency but risk producing inaccurate content, called "hallucinations." This paper outlines a standardized process for categorizing fine-grained hallucination types and proposes an innovative framework--the Progressive Fine-grained Model Editor (PFME)--specifically designed to detect and correct fine-grained hallucinations in LLMs. PFME consists of two collaborative modules: the Real-time Fact Retrieval Module and the Fine-grained Hallucination Detection and Editing Module. The former identifies key entities in the document and retrieves the latest factual evidence from credible sources. The latter further segments the document into sentence-level text and, based on relevant evidence and previously edited context, identifies, locates, and edits each sentence's hallucination type. Experimental results on FavaBench and FActScore demonstrate that PFME outperforms existing methods in fine-grained hallucination detection tasks. Particularly, when using the Llama3-8B-Instruct model, PFME's performance in fine-grained hallucination detection with external knowledge assistance improves by 8.7 percentage points (pp) compared to ChatGPT. In editing tasks, PFME further enhances the FActScore of FActScore-Alpaca13B and FActScore-ChatGPT datasets, increasing by 16.2pp and 4.6pp, respectively.

标准化的流程用于分类细粒度幻像类型并提出确定和修正LLM中细粒度幻像的创新框架PFME，由实时事实检索模块和细粒度幻像检测和编辑模块组成，在幻像检测和编辑任务中PFME表现出优于现有方法的性能。

PFME：一个用于大规模语言模型的细粒度幻觉检测和编辑的模块化方法