Assessing and Verifying Task Utility in LLM-Powered Applications
Negar Arabzadeh, Siqing Huo, Nikhil Mehta, Qingyun Wu, Chi Wang, Ahmed Hassan Awadallah, Charles L. A. Clarke, Julia Kiseleva
Abstract
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://github.com/Narabzad/AgentEval
BibTeX
@inproceedings{arabzadeh-etal-2024-assessing,
title = "Assessing and Verifying Task Utility in {LLM}-Powered Applications",
author = "Arabzadeh, Negar and
Huo, Siqing and
Mehta, Nikhil and
Wu, Qingyun and
Wang, Chi and
Awadallah, Ahmed Hassan and
Clarke, Charles L. A. and
Kiseleva, Julia",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1219/",
doi = "10.18653/v1/2024.emnlp-main.1219",
pages = "21868--21888"
}