Summary of P-ta: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation Via Large Language Models, by Shuo Yang et al.
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models
by Shuo Yang, Chenchen Yuan, Yao Rong, Felix Steinbauer, Gjergji Kasneci
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 proposed approach leverages Generative Adversarial Networks (GANs) and Large Language Models (LLMs) to generate accurate tabular data, addressing limitations of existing methodologies. By utilizing proximal policy optimization (PPO), the integration of GANs and LLMs enables the synthesis of high-quality tabular data, leading to a 4% improvement in model accuracy on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative method combines the strengths of GANs and LLMs to create realistic tabular data, which is crucial for many industries. By understanding how this technology works and its benefits, we can unlock new possibilities for businesses and organizations. |
Keywords
* Artificial intelligence * Optimization
