  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.
