Summary of What Affects the Stability Of Tool Learning? An Empirical Study on the Robustness Of Tool Learning Frameworks, by Chengrui Huang et al.
What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
by Chengrui Huang, Zhengliang Shi, Yuntao Wen, Xiuying Chen, Peng Han, Shen Gao, Shuo Shang
First submitted to arxiv on: 3 Jul 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 The paper explores how various factors affect the performance of large language models (LLMs) in interacting with real-world applications through tool learning methods. While existing works have fine-tuned LLMs or designed prompts to enable them to select and invoke tools correctly, the impact of different tasks, datasets, training settings, and algorithms on tool learning performance remains unclear. This paper investigates internal and external factors that influence tool learning frameworks, providing insights for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how language models can be taught to work with real-world applications using “tool learning” methods. Right now, some methods fine-tune the models or design special prompts to help them pick the right tools. But different tasks, datasets, and ways of training the models can affect how well they do this. The researchers wanted to see what factors make a difference in tool learning performance. They tested their ideas on two big datasets and found some important things that could help with future research. |
