  Large Language Models (LLMs) have shown proficiency in question-answering
tasks but often struggle to integrate real-time knowledge updates, leading to
potentially outdated or inaccurate responses. This problem becomes even more
challenging when dealing with multi-hop questions since they require LLMs to
update and integrate multiple knowledge pieces relevant to the questions. To
tackle the problem, we propose the Retrieval-Augmented model Editing (RAE)
framework tailored for multi-hop question answering. RAE first retrieves edited
facts and then refines the language model through in-context learning.
Specifically, our retrieval approach, based on mutual information maximization,
leverages the reasoning abilities of LLMs to identify chain facts that na\"ive
similarity-based searches might miss. Additionally, our framework incorporates
a pruning strategy to eliminate redundant information from the retrieved facts,
which enhances the editing accuracy and mitigates the hallucination problem.
Our framework is supported by theoretical justification for its fact retrieval
efficacy. Finally, comprehensive evaluation across various LLMs validates RAE's
ability in providing accurate answers with updated knowledge.
