Summary of Memorag: Moving Towards Next-gen Rag Via Memory-inspired Knowledge Discovery, by Hongjin Qian et al.
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery
by Hongjin Qian, Peitian Zhang, Zheng Liu, Kelong Mao, Zhicheng Dou
First submitted to arxiv on: 9 Sep 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 The proposed MemoRAG paradigm combines retrieval tools with long-term memory to enhance the generation quality of large language models (LLMs). This architecture consists of two systems: a light LLM for forming the global database memory, and an expensive but expressive LLM for generating answers based on retrieved information. The cluing mechanism is optimized to locate useful information in the database. MemoRAG outperforms existing RAG systems across various evaluation tasks, including complex ones where conventional RAG fails. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MemoRAG helps computers generate better answers by using a special kind of memory that stores lots of information. This new approach combines two types of language models to find and use relevant data from big databases. The system gets better at answering questions as it learns and remembers more things. MemoRAG does well on easy and hard tasks, making it a useful tool for computers. |
Keywords
» Artificial intelligence » Rag
