ToolFiVe: Enhancing Tool-Augmented LLMs via Tool Filtering and Verification

Abstract

Tool-augmented Large Language Models (LLMs) provide a robust theoretical foundation for AI agents, with the generation of reasoning plans being a crucial stage. Previous methods for generating reasoning plans primarily rely on In-Context Learning (ICL) or Supervised Fine-Tuning (SFT). However, methods based on ICL struggle with accurately utilizing tools, while those based on SFT face challenges in adapting to new toolsets and tasks. To address these issues, we introduce ToolFiVe, a general, plug-and-play, self-correction-based framework for leveraging specialized tools in compositional reasoning tasks. ToolFiVe redesigns the process of reasoning plan generation and integrates the tool-execution stage. Specifically, in the reasoning plan generation stage, ToolFiVe employs a filtering module to exclude task-irrelevant tools, creating a candidate toolset. ToolFiVe then constructs prompts using the candidate toolset and iteratively generates and refines the reasoning plan. Subsequently, a verification module evaluates the completeness of the reasoning plan, providing feedback for the self-correction loop. Ultimately, the reasoning plan developed during this stage guides the execution of the tools. Extensive experiments demonstrate that ToolFiVe outperforms other state-of-the-art (SOTA) methods, highlighting the significance of reasoning plan generation for general tool-augmented LLMs.