Summary of Retrieval Augmented Generation or Long-context Llms? a Comprehensive Study and Hybrid Approach, by Zhuowan Li et al.
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach
by Zhuowan Li, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive comparison is conducted between Retrieval Augmented Generation (RAG) and long-context (LC) Large Language Models (LLMs), leveraging the strengths of both. The study benchmarks RAG and LC across various public datasets using three latest LLMs, including Gemini-1.5 and GPT-4. The results show that when resourced sufficiently, LC consistently outperforms RAG in terms of average performance, but RAG’s significantly lower cost remains a distinct advantage. To address this, the authors propose Self-Route, a simple yet effective method that routes queries to RAG or LC based on model self-reflection. Self-Route significantly reduces computation cost while maintaining comparable performance to LC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can process long texts efficiently using a technique called Retrieval Augmented Generation (RAG). However, some models can understand long texts directly without needing this technique. Researchers compared these two approaches and found that when given enough resources, the direct approach worked better. But it was more expensive than RAG. To solve this problem, they came up with a new method called Self-Route. This method helps decide which approach to use based on how well each model can understand a piece of text. As a result, Self-Route uses less computer power while still getting good results. |
Keywords
» Artificial intelligence » Gemini » Gpt » Rag » Retrieval augmented generation
