The rapid advancements in large language models (LLMs) have presented
challenges in evaluating those models. Existing evaluation methods are either
reference-based or preference based, which inevitably need human intervention
or introduce test bias caused by evaluator models. In this paper, we propose
GameEval, a novel approach to evaluating LLMs through goal-driven
conversational games, overcoming the limitations of previous methods. GameEval
treats LLMs as game players and assigns them distinct roles with specific goals
achieved by launching conversations of various forms, including discussion,
question answering, and voting. We design three unique games with cooperative
or adversarial objectives, accompanied by corresponding evaluation metrics, to
show how this new paradigm comprehensively evaluates model performance.Through
extensive experiments, we show that GameEval can effectively differentiate the
capabilities of various LLMs, providing a comprehensive assessment of their
integrated abilities to solve complex problems. Our public anonymous code is
available at this https URL

通过目标驱动的对话游戏，GameEval 提出了一种新的评估大型语言模型的方法，能够全面评估模型的性能，展示其解决复杂问题的综合能力。