# Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation

**Authors:** Congjing Zhang, Ryan Feng Lin, Ruoxuan Bao, Shuai Huang  
**arXiv:** [2602.04785](https://arxiv.org/abs/2602.04785)  
**Date:** 2026-02-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2602.04785v1/

## Key points

- Challenges in obtaining high-quality tabular data for machine learning applications:
- Scarcity issues such as class imbalance, selection bias, and low fidelity
- Proposed solution: T$^2$ framework utilizing Large Language Models (LLMs) to synthesize high-quality tabular data
- Operates like an assembly line with specialized LLMs generating different components of synthetic data sequentially
- Three-stage plug-in data quality control pipeline to ensure high quality of synthetic data
- Empirical results demonstrate that T$^2 outperforms existing methods in producing high-quality tabular data
- Potential of T$^2 in supporting downstream models when direct data collection is impractical or infeasible

## Summary

The paper "Team-then-Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation" by Congjing Zhang, Ryan Feng Lin, Ruoxuan Bao, and Shuai Huang addresses the challenges of obtaining high-quality tabular data for machine learning applications. Tabular datasets are crucial for many real-world ML tasks but often suffer from scarcity issues such as class imbalance, selection bias, and low fidelity. To overcome these limitations, the authors propose a novel framework called T$^2$, which utilizes Large Language Models (LLMs) to synthesize high-quality tabular data. The T$^2$ framework operates like an assembly line where specialized LLMs collaborate to generate different components of the synthetic data sequentially. This process is guided by domain knowledge and is followed by a rigorous three-stage plug-in data quality control pipeline. By treating tabular data generation as a manufacturing process, T$^2 systematically evaluates the synthetic data across multiple dimensions of quality control. Empirical results from experiments on both simulated and real-world datasets demonstrate that T$^2 outperforms existing methods in producing high-quality tabular data. The framework shows promise in supporting downstream models when direct data collection is impractical or infeasible. Overall, this paper highlights the potential of leveraging collaborative LLMs and robust QC processes to address the challenges associated with acquiring quality tabular data for machine learning applications.