The Retrieval-Augmented model Editing (RAE) framework targets the challenge of updating real-time knowledge in LLMs, particularly for multi-hop question answering. Key insights include:

• Selective Fact Retrieval: Employs mutual information maximization to identify related chain facts, enhancing context understanding.
• Focused Editing: Prunes redundant information for higher accuracy and reduced hallucinations.
• Theoretical and Empirical Validation: Theoretical justification supports retrieval efficacy, with experimental results confirming improved answer accuracy.

Key Points:

• Mutual information-based retrieval is pivotal in identifying essential chain facts.
• Improved LLM performance on multi-hop question answering with real-time knowledge updates.
• Framework addresses the issue of integrating updated knowledge into complex queries.

RAE represents a significant advancement in the quest for real-time knowledge augmentation in LLMs, opening new avenues for research and practical applications in dynamic information retrieval scenarios. Explore the framework