Can multi-agent LLMs improve query analysis?
Multi-Agent Actor-Critic Generative AI for Query Resolution and Analysis
This paper introduces MASQRAD, a multi-agent system that translates imprecise user queries into precise visualizations and analyses using multiple specialized generative AI agents (actor, critic, expert). It aims to overcome common generative AI limitations like hallucinations and scalability issues while creating and validating complex analytical workflows.
Key to LLM-based multi-agent development: MASQRAD leverages LLMs like RoBERTa, LLaMA, GPT-3.5/4, and Claude to handle query interpretation, script generation, validation, and analysis. The critic agent uses multi-agent debate (MAD) to refine scripts through iterative feedback among multiple LLM instances, showcasing a novel collaborative approach to ensure code accuracy and efficiency. Prompt engineering is crucial for guiding each agent's task and maintaining alignment with system goals. The system's modular design allows for the integration of future LLMs and domain adaptation.