Papers by Yaobin Ling
MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing models for tabular data generation require large amounts of data to train effectively. |
| Approach: | They propose a framework to generate tabular data powered by large language models that emulates a Generative Adversarial Network. |
| Outcome: | The proposed framework outperforms state-of-the-art models while keeping privacy of real data. |