T -Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step (2024)

Abstract: Large language models (LLMs) 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.

## introduction large language models (llms) have fueled dramatic progress and emerged as a promising path to more advanced intelligence @xcite @xcite ). to further extend the capability of llms, tool utilization, which empowers llms to leverage external tools to solve more complicated problems, has spurred vast research interests in both research and industry @xcite @xcite . despite the attractive ability achieved by aiming llms with tools, how to evaluate llms in * equal contributions † corresponding author tool learning has not been fully explored. existing works evaluate the tool utilization ability based on the final output @xcite or only consider the single-step tool calling @xcite . however, real-world problems usually involve complex planning and executing multiple tools. simply judging the quality through the final output omits the assessment of the intermediate steps, making it hard to identify the main bottlenecks of the tool-use capability in llms. besides, current benchmarks mainly rely on real-time tool interactions @xcite , which overlooks the external factors (instability of api service or temporal information shift) on the overall judgment, leading to evaluation variance and unfair comparison. to overcome the above problems, we introduce t-eval, a step-by-step tool evaluation benchmark for llms. unlike prior works that appraise the model from a holistic perspective, we explicitly decompose the evaluation into several sub-tasks along the basic capabilities of the language model. specifically, given the golden tool-utilization annotations verified by human experts, we dedicatedly designed the evaluation protocols and corresponding instruction prompts based on the intermediate steps along the annotation path. such a paradigm enables us to separately benchmark each competence of the llms, including planning, reasoning, retrieval, understanding, instruction following, and review. additionally, thanks to the decomposed evaluation protocols, our benchmark significantly alleviates the exogenous influences (such as online tools) during the evaluation process, yielding a more stable and fair model assessment. by conducting extensive experiments on t-eval, we carry out in-depth analysis and insights on the results, pinpointing the main bottlenecks of current llms in tool learning. furthermore, we also prove that our benchmark reveals consistent evaluation of individual and comprehensive model abilities, where higher individual ability scores lead to better to respond to a query with a given tool list, llm agents generate a plan first before calling tools. the solution path is multiple rounds of tool calling where agents reason their thoughts, retrieve and understand the necessary tools and parameters, execute the instructions, and finally review the tool response. ## t-eval benchmarking llms as tool agents involves multiple dimensions of evaluations of llm abilities and suffers from the external influence of tools. therefore, we first thoroughly investigate each critical dimension of the tool-calling process ( §2.1), and then establish tailored evaluation protocols for each dimension ( §2.2), named t-eval, to enable a detailed evaluation of tool utilization capability. to guarantee the high quality of the golden solution paths and tool-calling responses, we adopt a humanin-the-loop data generation pipeline ( §2.3), ensuring the stability and longevity of t-eval. lastly, we provide a statistical overview of t-eval ( §2.4). ## conclusion in this paper, we propose t-eval, a comprehensive and fine-grained tool utilization evaluation benchmark for llms. t-eval explicitly disentangles the tool utilization tasks along the model ability, with dedicated evaluation protocols designed for respective tasks, unearthing the real ability of the evaluated models. such a step-wise evaluation delivers a thorough analysis and pinpoints the main bottlenecks of current llms in tool learning, providing valuable insights into further development of tool agents.