# Tool Learning with Foundation Models

**Authors:** Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang  
**arXiv:** [2304.08354](https://arxiv.org/abs/2304.08354)  
**Date:** 2023-04-17  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2304.08354v1/

## Key points

- Authors explore tool learning with foundation models to enhance problem-solving capabilities in AI systems
- Humans have remarkable ability to create and utilize tools, allowing them to overcome limitations and explore new frontiers
- Potential for AI systems to achieve similar levels of proficiency in tool use with foundation models
- Lack of comprehensive understanding regarding key challenges, opportunities, and future endeavors in tool learning with foundation models
- Systematic investigation of tool learning presented in the paper
- Background information provided on tool learning, including cognitive origins and paradigm shift brought about by foundation models
- Specialized tools and foundation models play complementary roles in achieving enhanced accuracy, efficiency, and automation in problem-solving
- Existing research on tool learning categorized into tool-augmented learning and tool-oriented learning
- General framework proposed for tool learning involving understanding user instructions, decomposing tasks into subtasks, adjusting plans through reasoning, and selecting appropriate tools for each subtask
- Training models discussed for improved tool-use capabilities and facilitating generalization in tool learning
- Emphasis on systematic evaluation in prior works related to tool learning; authors present their own experiments using 17 representative tools demonstrating potential of current foundation models
- Open problems identified that require further investigation in the field of tool learning
- Hope that the work will inspire future research efforts aimed at integrating tools with foundation models

## Summary

In this paper, the authors explore the concept of tool learning with foundation models, aiming to enhance problem-solving capabilities in AI systems. They highlight the remarkable ability of humans to create and utilize tools, which allows them to overcome physical limitations and explore new frontiers. With the emergence of foundation models, there is potential for AI systems to achieve similar levels of proficiency in tool use. The authors acknowledge that while tool learning with foundation models holds immense potential, there is still a lack of comprehensive understanding regarding key challenges, opportunities, and future endeavors in this field. To address this gap, they present a systematic investigation of tool learning. The paper begins by providing background information on tool learning, including its cognitive origins and the paradigm shift brought about by foundation models. The authors also discuss the complementary roles played by specialized tools and foundation models in achieving enhanced accuracy, efficiency, and automation in problem-solving. Next, they summarize existing research on tool learning into two categories: tool-augmented learning and tool-oriented learning. They propose a general framework for tool learning that involves understanding user instructions, decomposing complex tasks into subtasks, dynamically adjusting plans through reasoning, and effectively conquering each subtask by selecting appropriate tools. The authors also delve into training models for improved tool-use capabilities and facilitating generalization in tool learning. They emphasize the need for systematic evaluation in prior works related to tool learning and present their own experiments using 17 representative tools. These experiments demonstrate the potential of current foundation models in skillfully utilizing tools. Finally, the paper concludes with a discussion on several open problems that require further investigation in the field of tool learning. The authors hope that their work will inspire future research efforts aimed at integrating tools with foundation models. Overall, this paper provides a detailed exploration of tool learning with foundation models. It highlights the importance of understanding how humans utilize tools and proposes a framework for training AI systems to effectively use tools in problem-solving tasks.