Learning to Use Tools via Cooperative and Interactive Agents
Zhengliang Shi Shen Gao Xiuyi Chen Lingyong Yan Haibo Shi Dawei Yin Zhumin Chen Pengjie Ren Suzan Verberne Zhaochun Ren
Tool learning empowers large language models (LLMs) as agents to use external
tools to extend their capability. Existing methods employ one single LLM-based
agent to iteratively select and execute tools, thereafter incorporating the
result into the next action prediction. However, they still suffer from
potential performance degradation when addressing complex tasks due to: (1) the
limitation of the inherent capability of a single LLM to perform diverse
actions, and (2) the struggle to adaptively correct mistakes when the task
fails. To mitigate these problems, we propose the ConAgents, a Cooperative and
interactive Agents framework, which modularizes the workflow of tool learning
into Grounding, Execution, and Observing agents. We also introduce an iterative
calibration (IterCali) method, enabling the agents to adapt themselves based on
the feedback from the tool environment. Experiments conducted on three datasets
demonstrate the superiority of our ConAgents (e.g., 6 point improvement over
the SOTA baseline). We further provide fine-granularity analysis for the
efficiency and consistency of our framework.