Summary of Generating Realistic Tabular Data with Large Language Models, by Dang Nguyen et al.
Generating Realistic Tabular Data with Large Language Models
by Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Svetha Venkatesh
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 LLM-based method tackles the limitations of current generative models for tabular data by introducing three key improvements. First, it employs a novel permutation strategy during fine-tuning to capture correct correlations between features and target variables. Second, it utilizes feature-conditional sampling to generate synthetic samples that mimic real-world distributions. Third, it constructs prompts based on generated samples to query the fine-tuned LLM for accurate label generation. The method outperforms 10 state-of-the-art baselines across 20 datasets in downstream predictive tasks and produces highly realistic synthetic data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to generate tabular data using large language models (LLMs). Currently, most generative models are great at making images look real, but they’re not very good at creating fake tables. The authors’ method is better because it pays attention to the relationships between different pieces of information in the table. They do this by changing the way they train the model and by using clever tricks to make the generated data look more realistic. This new approach works really well, beating other methods on 20 different datasets and producing synthetic data that’s almost indistinguishable from real data. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Synthetic data
