Summary of Controlmath: Controllable Data Generation Promotes Math Generalist Models, by Nuo Chen et al.
ControlMath: Controllable Data Generation Promotes Math Generalist Models
by Nuo Chen, Ning Wu, Jianhui Chang, Jia Li
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposed ControlMath method utilizes large language models (LLMs) for data augmentation, enabling the generation of diverse math problems not limited to specific domains or distributions. The method involves an equation-generator module that creates diverse equations, which are then transformed into math word problems by a Problem-Crafter agent. A Reverse-Agent filters and selects high-quality data, achieving better results with fewer data points. This approach can help improve the model’s mathematical ability to generalize, leading to improved performances both within and beyond specific domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ControlMath is a new way to create math problems using artificial intelligence. It takes large language models (LLMs) and uses them to generate different types of math equations. These equations are then turned into word problems that can be used to test how well AI models can solve math problems. The best part is that these word problems aren’t limited to specific topics or levels, making it a powerful tool for teaching and learning math. |
Keywords
» Artificial intelligence » Data augmentation
