  Large language models (LLMs) have demonstrated exceptional reasoning
capabilities, enabling them to solve various complex problems. Recently, this
ability has been applied to the paradigm of tool learning. Tool learning
involves providing examples of tool usage and their corresponding functions,
allowing LLMs to formulate plans and demonstrate the process of invoking and
executing each tool. LLMs can address tasks that they cannot complete
independently, thereby enhancing their potential across different tasks.
However, this approach faces two key challenges. First, redundant error
correction leads to unstable planning and long execution time. Additionally,
designing a correct plan among multiple tools is also a challenge in tool
learning. To address these issues, we propose Tool-Planner, a task-processing
framework based on toolkits. Tool-Planner groups tools based on the API
functions with the same function into a toolkit and allows LLMs to implement
planning across the various toolkits. When a tool error occurs, the language
model can reselect and adjust tools based on the toolkit. Experiments show that
our approach demonstrates a high pass and win rate across different datasets
and optimizes the planning scheme for tool learning in models such as GPT-4 and
Claude 3, showcasing the potential of our method.
