Long and Light RAG: Enhancing LLMs with Scientific Literature
A deep dive into Long RAG and Light RAG, two state-of-the-art frameworks that enhance LLMs by integrating external knowledge during response generation.
Course Timeline
🎥 Introduction to RAG and its Challenges
Overview of Retrieval Augmented Generation (RAG) systems, their traditional pipeline, and the limitations of using small information units.
🚀 Long RAG: Enhancing Context with Longer Documents
Explanation of Long RAG's approach of processing longer document segments (4000 tokens) to improve context retention and accuracy. Includes performance comparison with baselines.
💡 Light RAG: Graph-Based Retrieval for Enhanced Understanding
Introduction to Light RAG's graph-based retrieval system, addressing the issues of flat data representation and lack of interdependent knowledge structure. Details its four-stage process.
📊 Comparative Analysis of RAG Models
Comparison of Light RAG's performance against baselines (Naive RAG, RQ RAG, HyDE, Graph RAG) across various datasets, highlighting its strengths in comprehensiveness and diversity.
🤔 Key Takeaways and Conclusion
Summary of the key findings, comparing the strengths of Long RAG (simplicity) and Light RAG (graph-based approach), and discussing the future of RAG advancements.