Large Language Models (LLMs) have static knowledge, making updates costly and time-consuming. Retrieval-augmented generation (RAG) helps, but irrelevant info can degrade performance.
Solution: Retrieval Augmented Iterative Self-Feedback (RA-ISF) refines RAG by breaking tasks into subtasks. It uses: 1. Task Decomposition: Splits tasks into subtasks. 2. Knowledge Retrieval: Fetches relevant info for each subtask. 3. Response Generation: Integrates info to generate accurate answers. What’s Next: RA-ISF reduces hallucinations and boosts performance, enhancing LLMs for complex tasks. As it evolves, expect more powerful, knowledge-enhanced LLMs.
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