Language Model Council: A Multi-Agent Framework using Explainable AI

Abstract

The rapid advancement of Large Language Models (LLMs) has significantly expanded the capabilities of Artificial Intelligence in language understanding, reasoning, and automated decision support. Despite these achievements, systems built around a single language model remain vulnerable to problems such as factual inaccuracies, hallucinated information, inconsistent outputs, limited explainability, and unintended bias. These shortcomings restrict their use in applications where decisions must be accurate, transparent, and accountable. This work introduces the Language Model Council (LMC), a collaborative framework that combines the expertise of multiple specialized AI agents to evaluate a user query from different perspectives. Their independent analyses are consolidated through a consensus-driven mechanism that selects the most reliable response. To further improve transparency, the framework integrates Explainable Artificial Intelligence (XAI), providing confidence estimates together with concise reasoning summaries that clarify how the final decision was derived. Experimental evaluation indicates that the proposed approach outperforms traditional single-model systems by improving response quality, reducing hallucinations, and increasing user trust through enhanced explainability.