Summary of Unveiling and Consulting Core Experts in Retrieval-augmented Moe-based Llms, by Xin Zhou et al.
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
by Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper investigates the internal mechanisms within Large Language Models (LLMs) that contribute to the effectiveness of Retrieval-Augmented Generation (RAG) systems, focusing on Mixture-of-Expert (MoE)-based LLMs. It reveals that several core groups of experts are responsible for RAG-related behaviors and proposes strategies to enhance RAG’s efficiency and effectiveness through expert activation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study improves our understanding of how Large Language Models work with external knowledge, allowing them to solve complex tasks better. Researchers found that certain “experts” within the model are important for this process, and by understanding how these experts interact, they can make the models more effective. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
