Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debates

The rapid advancement of large language models (LLMs) has revolutionized the field of natural language processing, enabling the development of increasingly sophisticated AI systems capable of generating human-like text, engaging in dialogue, and performing complex language tasks [5]. As these models grow in size and capability, the challenge of accurately evaluating their performance and aligning their outputs with human preferences has become increasingly critical [3, 15, 49]. Traditional evaluation methods, such as human assessments and automated metrics, often struggle to capture the nuances and complexities of LLM outputs, leading to a gap between model performance and user expectations [7, 17, 24]. Human evaluations are time-consuming, expensive, and prone to inconsistency and bias [12, 27], while automated metrics frequently fail to align with human judgments, particularly in open-ended generation tasks [29, 13, 22]. To address these challenges, we propose a novel framework for evaluating LLM outputs using LLMs themselves as interacting agents in a courtroom-inspired, multi-agent system. Our approach draws inspiration from various fields, including decision theory, economics, psychology, legal theory, and voting theory, to develop a more dynamic, contextual, and comprehensive assessment process.