Summary of Clustered Retrieved Augmented Generation (crag), by Simon Akesson and Frances A. Santos
Clustered Retrieved Augmented Generation (CRAG)
by Simon Akesson, Frances A. Santos
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach called CRAG (Retrieval Augmented Generation) to provide external knowledge to Large Language Models (LLMs). The goal is to incorporate up-to-date content, domain-specific knowledge, and prevent hallucinations. The RAG approach has been widely adopted, but it may not be feasible for some applications due to the context window size limitations. CRAG aims to reduce the number of prompting tokens without degrading response quality compared to RAG. Experiments show that CRAG can reduce token counts by at least 46%, achieving over 90% in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better use Large Language Models (LLMs) in real-life situations. Right now, we need to find and give them new information quickly, about specific topics, and make sure they don’t get too creative with their answers. The current way of doing this is good, but it has some problems. Sometimes the information needed is too big for the LLM to handle, or giving them all that info takes a long time. To solve these issues, the researchers came up with a new method called CRAG. It can give LLMs the right amount of information without making their answers worse. The scientists tested this and found out that CRAG can reduce the amount of information by at least 46% while still getting good results. |
Keywords
» Artificial intelligence » Context window » Prompting » Rag » Retrieval augmented generation » Token
