Retrieval Augmented Iterative Self-Feedback (RA-ISF) refines RAG by breaking tasks into subtasks.

🚀 Problem: 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.

RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback