Large language models (LLM) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool-utilization capability of LLMs is still under-explored. In contrast to previous works that evaluate models holistically, we comprehensively decompose the tool utilization into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. Based on that, we further introduce T-Eval to evaluate the tool utilization capability step by step. T-Eval disentangles the tool utilization evaluation into several sub-domains along model capabilities, facilitating the inner understanding of both holistic and isolated competency of LLMs. We conduct extensive experiments on T-Eval and in-depth analysis of various LLMs. T-Eval not only exhibits consistency with the outcome-oriented evaluation but also provides a more fine-grained analysis of the capabilities of LLMs, providing a new perspective in LLM evaluation on tool-utilization ability. The benchmark will be available at https://github.com/open-compass/T-Eval.
@article{arxiv.2312.14033,
title = {T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step},
author = {Zehui Chen and Weihua Du and Wenwei Zhang and Kuikun Liu and Jiangning Liu and Miao Zheng and Jingming Zhuo and Songyang Zhang and Dahua Lin and Kai Chen and Feng Zhao},
journal= {arXiv preprint arXiv:2312.14033},
year = {2024}
}