
The widespread adoption of Large Language Models (LLMs) in real-world applications has been evident in recent years. However, these models are typically limited in their performance on higher-level tasks, such as learning to use external tools like APIs. Previous work has attempted to construct instruction tuning data for tool use, but these efforts have been hindered by their failure to fully stimulate tool-use capabilities. They are also constrained by l











