Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. This paradigm, i.e., tool learning with foundation models, combines the strengths of specialized tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field. To this end, we present a systematic investigation of tool learning in this paper. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research into tool-augmented and tool-oriented learning. We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate the generalization in tool learning. Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 17 representative tools and show the potential of current foundation models in skillfully utilizing tools. Finally, we discuss several open problems that require further investigation for tool learning. Overall, we hope this paper could inspire future research in integrating tools with foundation models.

翻译：人类拥有创造和使用工具的非凡能力，这使他们能够克服身体限制并探索新边界。随着基础模型的出现，人工智能系统在工具使用方面有望与人类同样熟练。这种范式，即基础模型中的工具学习，结合了专用工具和基础模型的优势，在问题解决中实现了更高的准确性、效率和自动化。尽管其潜力巨大，但该领域的关键挑战、机遇和未来方向仍缺乏全面理解。为此，本文对工具学习进行了系统性研究。我们首先介绍了工具学习的背景，包括其认知起源、基础模型的范式转变，以及工具与模型的互补作用。然后，我们将现有的工具学习研究归纳为工具增强学习和工具导向学习。我们提出了一个通用的工具学习框架：从理解用户指令开始，模型应学会将复杂任务分解为若干子任务，通过推理动态调整计划，并通过选择合适工具有效攻克每个子任务。我们还讨论了如何训练模型以提升其工具使用能力，并促进工具学习中的泛化。鉴于以往研究缺乏系统性的工具学习评估，我们使用17种代表性工具进行实验，展示了当前基础模型在熟练使用工具方面的潜力。最后，我们讨论了工具学习中需要进一步研究的若干开放性问题。总体而言，希望本文能激发未来关于工具与基础模型融合的研究。