Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving nine popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers that follow ChatGPT to provide detailed descriptions that can enhance the tool selection performance of LLMs.

翻译：大型语言模型（LLM）因其卓越的自然语言处理能力而备受关注。近期，众多研究聚焦于LLM的工具使用能力，主要探讨LLM如何与特定工具高效协作。然而，在LLM作为智能代理的场景中（如AutoGPT和MetaGPT等应用），LLM需要参与复杂的决策过程，包括决定是否使用工具，以及从可用工具集合中选择最适合的工具以满足用户需求。为此，本文提出MetaTool基准测试，旨在评估LLM是否具备工具使用意识并能正确选择工具。具体而言，我们在该基准中构建了名为ToolE的数据集，包含触发LLM使用工具的各种类型用户查询提示，涵盖单工具和多工具场景。随后，我们设定了工具使用意识与工具选择两大任务，并从不同维度定义了四个子任务：相似选项下的工具选择、特定场景中的工具选择、存在可靠性问题的工具选择以及多工具选择。我们对九种主流LLM进行了实验，发现大多数模型仍难以有效选择工具，凸显了LLM与真正智能代理之间的差距。然而，通过错误分析，我们发现仍存在显著的改进空间。最后，我们为工具开发者提供了启示：遵循ChatGPT的实践，提供详细的工具描述可提升LLM的工具选择性能。