Summary of Dynamic Multi-agent Orchestration and Retrieval For Multi-source Question-answer Systems Using Large Language Models, by Antony Seabra et al.
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models
by Antony Seabra, Claudio Cavalcante, Joao Nepomuceno, Lucas Lago, Nicolaas Ruberg, Sergio Lifschitz
First submitted to arxiv on: 23 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 A novel methodology is proposed to develop robust, multi-source question-answer systems by combining advanced techniques in Large Language Models (LLMs). The approach integrates information from diverse data sources, including unstructured documents and structured databases, through a coordinated multi-agent orchestration and dynamic retrieval strategy. Specialized agents are designed to dynamically select the most appropriate retrieval strategy based on query nature. Dynamic prompt engineering is employed to adapt prompts in real-time to query-specific contexts. This methodology demonstrates enhanced response accuracy and relevance within the domain of Contract Management, where complex queries require seamless interaction between unstructured and structured data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help computers answer questions by combining different techniques. It’s like having many small computers working together to find the best information from lots of different sources. These sources can include things like documents and databases. The approach uses special agents that figure out which source is best for each question. It also adjusts its questions in real-time to get more accurate answers. |
Keywords
» Artificial intelligence » Prompt
