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（LLMs）因其卓越的自然语言处理（NLP）能力而备受关注。近期，许多研究聚焦于LLMs的工具使用能力，主要探讨了LLMs如何有效协同特定工具。然而，在LLMs作为智能体的场景中（例如AutoGPT和MetaGPT等应用），LLMs需要执行涉及复杂决策的过程——包括判断是否需调用工具，以及从可用工具集合中选出最合适的工具来满足用户请求。为此，本文提出MetaTool基准，旨在评估LLMs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，包含多种以提示形式触发LLMs使用工具的用户查询，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，并从不同角度定义了工具选择的四个子任务：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。通过对九种主流LLMs进行实验，我们发现大多数模型在有效选择工具方面仍存在困难，凸显了当前LLMs与真正智能体之间的差距。然而，通过误差分析，我们意识到仍有显著的改进空间。最后，我们向工具开发者提出建议：遵循ChatGPT的模式提供详细描述，可提升LLMs的工具选择性能。