  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.
