Tool-Planner: Task Planning with Clusters across Multiple Tools

TL;DR

Tool-Planner enhances large language models' task planning by grouping tools into clusters based on API functions, enabling more stable, efficient, and adaptable multi-tool usage, demonstrated with improved performance on various datasets.

Contribution

Introduces a toolkit-based framework that clusters tools by function, improving planning stability and efficiency in multi-tool task execution for LLMs.

Findings

High pass and win rates across datasets

Optimized planning schemes for GPT-4 and Claude 3

Effective tool re-selection and adjustment

Abstract

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. Our code is public at https://github.com/OceannTwT/Tool-Planner