Summary of Tool Learning with Large Language Models: a Survey, by Changle Qu et al.
Tool Learning with Large Language Models: A Survey
by Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, Ji-Rong Wen
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This comprehensive survey aims to bridge the gap in tool learning with large language models (LLMs), exploring both the benefits and implementation of this paradigm. The authors review existing literature, focusing on six aspects highlighting the advantages of tool integration and the inherent benefits of tool learning. The survey is structured around four key stages: task planning, tool selection, tool calling, and response generation. Additionally, it provides a detailed summary of existing benchmarks and evaluation methods. While discussing current challenges, the authors outline potential future directions for researchers and industrial developers to further explore this emerging area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tool learning with large language models (LLMs) is an exciting new area that can help tackle complex problems. This survey looks at what makes tool learning useful and how it’s done. The authors review many studies on the topic, grouping them into four stages: planning tasks, choosing tools, using tools, and generating responses. They also discuss existing ways to measure progress and performance. Finally, they talk about current challenges and potential future directions for researchers and developers. |
