ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback
Qinzhuo Wu, Wei Liu, Jian Luan, Bin Wang · 2024
Recently, tool-augmented LLMs have gained increasing attention.Given an instruction, toolaugmented LLMs can interact with various external tools in multiple rounds and provide a final answer.However, previous LLMs were trained on overly detailed instructions, which included API names or parameters, while real users would not explicitly mention these API details.This leads to a gap between trained LLMs and real-world scenarios.In addition, most works ignore whether the interaction process follows the instruction.To address these issues, we constructed a training dataset called MGToolBench, which contains statement and category-level instructions to better reflect realworld scenarios.In addition, we propose Tool-Planner, a two-stage reinforcement learning framework that utilizes path planning and two feedback mechanisms to enhance the LLM's task completion and instruction-following capabilities.Experimental results show that Tool-Planner significantly improves the Match Rate, Pass Rate and Win Rate by 26.8%, 20.2%, and 5.6% compared to the SOTA model.Human evaluation verifies that the multi-granularity instructions can better align with users' usage habits.Our data and code are available at https://github.com/XiaoMi/toolplanner.