The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks. However, a significant gap remains in assessing whether LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the pressing need for methods to verify utility of LLM-powered applications, particularly by ensuring alignment between the application's functionality and end-user needs. We introduce AgentEval provides an implementation for the math problems}, a novel framework designed to simplify the utility verification process by automatically proposing a set of criteria tailored to the unique purpose of any given application. This allows for a comprehensive assessment, quantifying the utility of an application against the suggested criteria. We present a comprehensive analysis of the robustness of quantifier's work.

翻译：大语言模型领域的快速发展催生了大量促进多智能体协作以辅助人类日常任务的应用程序。然而，在评估这些基于大语言模型的应用是否能真正提升用户体验与任务执行效率方面，仍存在显著差距。这凸显了验证此类应用效用的迫切需求，尤其是确保应用功能与最终用户需求的一致性。我们提出了AgentEval框架——一种新型方法论，旨在通过自动为任意给定应用量身定制一套评估标准来简化效用验证过程。该框架能够基于建议标准对应用效用进行量化评估，从而实现全面分析。我们针对量化器工作鲁棒性进行了全面分析。