Enhancing ML Explainability with Multi-Agent LLMs: A Context-Aware XAI Approach - Miyaji, Renato O.; Corrêa, Pedro L. P. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais do XL Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2025.246998, 2025)
Explainable AI (XAI) techniques enhance ML interpretability but often require technical expertise. We propose a multi-agent architecture that improves LLM-generated explanations through structured reasoning and contextual retrieval. Our system integrates web search, Retrieval-Augmented Generation (RAG), and XAI outputs via specialized agents. Experiments on the Adult dataset show that our approach outperforms standard LLM explanations by 7% in Context Awareness. Additionally, we validate LLM as a Judge, achieving over 80% correlation with human evaluations. Different LLMs, including OpenAI’s GPT-4o and GPT-4o-mini, were tested, highlighting the effectiveness of multi-agent systems in ML explainability.