Summary of Budget-constrained Tool Learning with Planning, by Yuanhang Zheng et al.
Budget-Constrained Tool Learning with Planning
by Yuanhang Zheng, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| 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 novel approach for budget-constrained tool learning proposed in this paper creates a preferable plan under budget constraints before utilizing tools. This plan outlines feasible tools and their maximum usage, providing a comprehensive overview of the process for large language models. The method involves initially estimating candidate tool usefulness based on past experience and then using dynamic programming to formulate the plan. Experimental results show that the approach can be integrated with various tool learning methods, significantly enhancing effectiveness under budget constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to learn tools within a set budget. Imagine you have a certain amount of money to spend on tools, and you want to use them wisely. This paper shows how to create a plan that tells you which tools are best to use and when. The plan takes into account the cost of each tool and helps you make the most of your budget. The researchers tested their method and found that it works well with different learning methods. |
