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arXiv · 2402.09015

Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications

Negar Arabzadeh·Julia Kiseleva·Qingyun Wu·Chi Wang·Ahmed Awadallah·Victor Dibia·Adam Fourney·Charles Clarke
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Abstract

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.

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Negar Arabzadeh, Julia Kiseleva, Qingyun Wu, Chi Wang, Ahmed Awadallah, Victor Dibia, Adam Fourney, Charles Clarke. 2024-02-14. Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications. https://arxiv.org/abs/2402.09015

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