Recently, tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization, posing barriers to entry for newcomers. This gap motivates us to conduct a comprehensive survey of existing works on tool learning with LLMs. In this survey, we focus on reviewing existing literature from the two primary aspects (1) why tool learning is beneficial and (2) how tool learning is implemented, enabling a comprehensive understanding of tool learning with LLMs. We first explore the "why" by reviewing both the benefits of tool integration and the inherent benefits of the tool learning paradigm from six specific aspects. In terms of "how", we systematically review the literature according to a taxonomy of four key stages in the tool learning workflow: task planning, tool selection, tool calling, and response generation. Additionally, we provide a detailed summary of existing benchmarks and evaluation methods, categorizing them according to their relevance to different stages. Finally, we discuss current challenges and outline potential future directions, aiming to inspire both researchers and industrial developers to further explore this emerging and promising area. We also maintain a GitHub repository to continually keep track of the relevant papers and resources in this rising area at \url{https://github.com/quchangle1/LLM-Tool-Survey}.

翻译：近年来，大语言模型（LLMs）的工具学习已成为一种增强LLMs处理高度复杂问题能力的前沿范式。尽管该领域日益受到关注且发展迅速，但现有文献仍较为零散，缺乏系统性梳理，这为初学者进入该领域设置了障碍。这一现状促使我们对LLMs工具学习的现有工作进行全面综述。本综述聚焦于从两个核心方面回顾现有文献：（1）为何工具学习具有优势；（2）如何实现工具学习，从而全面理解LLMs工具学习。我们首先通过六个具体维度，从工具集成的益处和工具学习范式本身的内在优势两方面探讨“为何”需要工具学习。在“如何”实现方面，我们依据工具学习工作流程的四个关键阶段——任务规划、工具选择、工具调用和响应生成——构建分类体系，系统性地回顾相关文献。此外，我们详细总结了现有基准测试和评估方法，并根据其与不同阶段的关联性进行分类。最后，我们讨论了当前面临的挑战并展望了未来潜在发展方向，旨在启发学术界和工业界的研究者进一步探索这一新兴且前景广阔的领域。我们同时维护了一个GitHub仓库，持续追踪该新兴领域的相关论文与资源，地址为 \url{https://github.com/quchangle1/LLM-Tool-Survey}。