MetaTool: Facilitating Large Language Models to Master Tools with Meta-task Augmentation
- URL: http://arxiv.org/abs/2407.12871v1
- Date: Mon, 15 Jul 2024 10:15:41 GMT
- Title: MetaTool: Facilitating Large Language Models to Master Tools with Meta-task Augmentation
- Authors: Xiaohan Wang, Dian Li, Yilin Zhao, Sinbadliu, Hui Wang,
- Abstract summary: We introduce a new tool learning methodology (MetaTool) that is generalizable for mastering any reusable toolset.
We develop a series of meta-tasks that involve predicting masked factors of tool execution.
By incorporating meta-task data into the instruction tuning process, the proposed MetaTool model achieves significant superiority to open-source models.
- Score: 25.360660222418183
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Utilizing complex tools with Large Language Models (LLMs) is a critical component for grounding AI agents in various real-world scenarios. The core challenge of manipulating tools lies in understanding their usage and functionality. The prevailing approach involves few-shot prompting with demonstrations or fine-tuning on expert trajectories. However, for complex tools and tasks, mere in-context demonstrations may fail to cover sufficient knowledge. Training-based methods are also constrained by the high cost of dataset construction and limited generalizability. In this paper, we introduce a new tool learning methodology (MetaTool) that is generalizable for mastering any reusable toolset. Our approach includes a self-supervised data augmentation technique that enables LLMs to gain a comprehensive understanding of various tools, thereby improving their ability to complete tasks effectively. We develop a series of meta-tasks that involve predicting masked factors of tool execution. These self-supervised tasks enable the automatic generation of high-quality QA data concerning tool comprehension. By incorporating meta-task data into the instruction tuning process, the proposed MetaTool model achieves significant superiority to open-source models and is comparable to GPT-4/GPT-3.5 on multiple tool-oriented tasks.
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