Assessing and Verifying Task Utility in LLM-Powered Applications
This addresses the need for verifying utility in LLM applications to ensure alignment with user needs, though it is incremental as it builds on existing verification methods.
The paper tackles the problem of assessing whether LLM-powered applications genuinely enhance user experience and task efficiency by introducing AgentEval, a framework that automatically proposes tailored criteria for utility verification, and demonstrates its effectiveness on datasets like Math Problem solving and ALFWorld tasks with publicly available data and code.
The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. However, a significant gap remains in assessing to what extent LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the need to verify utility of LLM-powered applications, particularly by ensuring alignment between the application's functionality and end-user needs. We introduce AgentEval, 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 effectiveness and robustness of AgentEval for two open source datasets including Math Problem solving and ALFWorld House-hold related tasks. For reproducibility purposes, we make the data, code and all the logs publicly available at https://bit.ly/3w3yKcS .