Summary of A Collaborative Multi-agent Approach to Retrieval-augmented Generation Across Diverse Data, by Aniruddha Salve et al.
A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data
by Aniruddha Salve, Saba Attar, Mahesh Deshmukh, Sayali Shivpuje, Arnab Mitra Utsab
First submitted to arxiv on: 8 Dec 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 This paper presents a novel approach to enhance Large Language Models (LLMs) by incorporating external, domain-specific data into the generative process. The proposed Retrieval-Augmented Generation (RAG) system tackles limitations of traditional RAG systems, which often rely on static pre-trained datasets and struggle with diverse data sources such as relational databases, document stores, and graph databases. To address these challenges, the authors propose a multi-agent RAG system that consists of specialized agents optimized for specific data sources. Each agent handles query generation for its respective domain, collaborating within a modular framework to ensure efficient query execution and improved response accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to help big language models learn from different types of data. Right now, these models rely on fixed datasets that were prepared beforehand, which limits their ability to use new or private information. The authors suggest a system called Retrieval-Augmented Generation (RAG) that can handle various data sources like databases and document stores. Instead of using one approach for all types of data, RAG uses different “agents” each designed for a specific type of data. This allows the agents to work together efficiently and accurately, making it easier to use big language models in situations where they need to learn from diverse or private data sources. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
