How to improve LLM knowledge base with feedback?
STACKFEED: STRUCTURED TEXTUAL ACTOR-CRITIC KNOWLEDGE BASE EDITING WITH FEEDBACK
October 15, 2024
https://arxiv.org/pdf/2410.10584-
Main Topic: This paper proposes STACKFEED, a system for improving the accuracy of Retrieval-Augmented Generation (RAG) systems by automatically refining knowledge bases (KB) using expert feedback.
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Key Points for LLM-based Multi-Agent Systems:
- STACKFEED employs a multi-agent reinforcement learning framework with a central critic (for global feedback analysis) and document-specific actors (to execute edits).
- Emphasizes structured editing of KB documents, making edits targeted and manageable even for large documents.
- Proposes metrics to evaluate the quality of KB edits in terms of completeness, generalizability, and coherence.
- Could be useful for LLM applications requiring dynamic KB updates without full model retraining, like chatbots or code generation tools.