Summary of Duetrag: Collaborative Retrieval-augmented Generation, by Dian Jiao et al.
DuetRAG: Collaborative Retrieval-Augmented Generation
by Dian Jiao, Li Cai, Jingsheng Huang, Wenqiao Zhang, Siliang Tang, Yueting Zhuang
First submitted to arxiv on: 12 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 The proposed Collaborative Retrieval-Augmented Generation (DuetRAG) framework addresses issues of irrelevant knowledge retrieval in complex domain questions for Large Language Models (LLMs). By integrating domain fine-tuning and RAG models, DuetRAG improves knowledge retrieval quality and generation quality. This is demonstrated through matching with expert human researchers on the HotPot QA benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DuetRAG helps computers understand tricky questions better by combining two ideas: giving a language model more information to work with, and making sure that information is relevant. Right now, this approach isn’t very good at handling complex topics because it can’t always find the right information. The new framework tries to fix this problem by using both domain-specific knowledge and the original language model. This makes the generated answers much better. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Rag » Retrieval augmented generation
