Summary of Mallm-gan: Multi-agent Large Language Model As Generative Adversarial Network For Synthesizing Tabular Data, by Yaobin Ling et al.
MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data
by Yaobin Ling, Xiaoqian Jiang, Yejin Kim
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework generates synthetic tabular data, leveraging large language models (LLMs) to emulate a Generative Adversarial Network (GAN). This approach enhances the quality of synthetic data generation in scenarios with small sample sizes, utilizing LLM as an optimizer and incorporating data generation process as contextual information. The model outperforms state-of-the-art methods regarding generating high-quality synthetic data for downstream tasks while preserving real data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem: accessing data is crucial for research, but it’s often private or expensive to get. To fix this, the authors created a new way to make fake data that’s really good and uses large language models. This makes it possible to create high-quality synthetic data even when you only have a little bit of real data. The results show that their method works better than other methods at making fake data that’s useful for research. |
Keywords
» Artificial intelligence » Gan » Generative adversarial network » Synthetic data
