  Augmenting large language models (LLMs) with external tools has emerged as a
promising approach to extending the capability of LLMs. Although some works
employ open-source LLMs for the tool learning task, most of them are trained in
a controlled environment in which LLMs only learn to execute the human-provided
tools. However, selecting proper tools from the large toolset is also a crucial
ability for the tool learning model to be applied in real-world applications.
Existing methods usually directly employ self-instruction methods to train the
model, which ignores differences in tool complexity. In this paper, we propose
the Confucius, a novel tool learning framework to train LLM to use complicated
tools in real-world scenarios, which contains two main phases: (1) We first
propose a multi-stage learning method to teach the LLM to use various tools
from an easy-to-difficult curriculum; (2) thenceforth, we propose the Iterative
Self-instruct from Introspective Feedback (ISIF) to dynamically construct the
dataset to improve the ability to use the complicated tool. Extensive
experiments conducted on both controlled and real-world settings demonstrate
the superiority of our tool learning framework in the real-world application
scenarios compared to both tuning-free (e.g. ChatGPT, Claude) and tuning-based
baselines (e.g. GPT4Tools).
