Multi-Agent Systems for Improved Information Retrieval - Leveraging Autonomous Agents and LLM Models
In the era of dynamic technological development and growing needs for data processing and analysis, the architecture of multi-agent systems is gaining importance. These systems, combined with Large Language Models (LLMs), offer an innovative approach to the information retrieval process that can enhance the efficiency, speed, and reliability of fact-finding and question-answering. This paper proposes the use of a designed multi-agent system architecture that uses autonomous agents and LLM models to efficiently acquire and process large amounts of data. It is shown how the integration of these technologies allows for more effective and precise information acquisition, which can lead to innovative solutions in both business and science. The effectiveness of the solution was evaluated using specific metrics and testing.
Sun 16 NovDisplayed time zone: Seoul change
11:00 - 12:00 | Session 2: Retrieval-Augmented Intelligence and Code GenerationMAS-GAIN at Grand Hall 6 Chair(s): Vittoriano Muttillo University of Teramo | ||
11:00 20mFull-paper | Multi-Agent Systems for Improved Information Retrieval - Leveraging Autonomous Agents and LLM Models MAS-GAIN Aneta Poniszewska-Maranda Institute of Information Technology, Lodz University of Technology, Maciej Kopa Lodz University of Technology, Bozena Borowska Institute of Information Technology, Lodz University of Technology | ||
11:20 20mFull-paper | GRACG: Graph Retrieval Augmented Code Generation MAS-GAIN | ||
11:40 15mShort-paper | Bridging the Prototype-Production Gap: A Multi-Agent System for Notebooks Transformation MAS-GAIN Hanya Elhashemy Siemens AG, Youssef Lotfy Technical University of Munich (TUM) / Siemens AG, Yongjian Tang Siemens AG, Germany | ||