The distinction between humans and animals lies in the unique ability of humans to use and create tools. Tools empower humans to overcome physiological limitations, fostering the creation of magnificent civilizations. Similarly, enabling foundational models like Large Language Models (LLMs) with the capacity to learn external tool usage may serve as a pivotal step toward realizing artificial general intelligence. Previous studies in this field have predominantly pursued two distinct approaches to augment the tool invocation capabilities of LLMs. The first approach emphasizes the construction of relevant datasets for model fine-tuning. The second approach, in contrast, aims to fully exploit the inherent reasoning abilities of LLMs through in-context learning strategies. In this work, we introduce a novel tool invocation pipeline designed to control massive real-world APIs. This pipeline mirrors the human task-solving process, addressing complicated real-life user queries. At each step, we guide LLMs to summarize the achieved results and determine the next course of action. We term this pipeline `from Summary to action', Sum2Act for short. Empirical evaluations of our Sum2Act pipeline on the ToolBench benchmark show significant performance improvements, outperforming established methods like ReAct and DFSDT. This highlights Sum2Act's effectiveness in enhancing LLMs for complex real-world tasks.

人类与动物之间的区别在于人类可以使用和创造工具，而使大型语言模型具备学习外部工具使用的能力可以成为实现人工智能的重要一步。本研究引入了一种新的工具调用管道，设计用于控制庞大的真实世界应用程序接口，通过`从摘要到行动'的Sum2Act管道，在复杂的真实用户查询中模拟人类解决问题的过程，从而提高了大型语言模型的性能，优于现有的方法。

从总结到行动：利用开放式接口增强大规模语言模型对复杂任务的应用