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

• URL: http://arxiv.org/abs/2410.04663v2
• Date: Thu, 24 Oct 2024 21:42:20 GMT
• Title: Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debates
• Authors: Chaithanya Bandi, Abir Harrasse,
• Abstract summary: We propose a framework that interprets large language models (LLMs) as advocates within an ensemble of interacting agents. This approach offers a more dynamic and comprehensive evaluation process compared to traditional human-based assessments or automated metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores optimal architectures for evaluating the outputs of large language models (LLMs) using LLMs themselves. We propose a novel framework that interprets LLMs as advocates within an ensemble of interacting agents, allowing them to defend their answers and reach conclusions through a judge and jury system. This approach offers a more dynamic and comprehensive evaluation process compared to traditional human-based assessments or automated metrics. We discuss the motivation behind this framework, its key components, and comparative advantages. We also present a probabilistic model to evaluate the error reduction achieved by iterative advocate systems. Finally, we outline experiments to validate the effectiveness of multi-advocate architectures and discuss future research directions.

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