Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation
This addresses the challenge of acquiring reliable tabular data for machine learning applications when direct collection is infeasible, though it appears incremental as it builds on existing LLM advances.
The paper tackles the problem of generating high-quality tabular data, which is often scarce and flawed, by introducing the Team-then-Trim framework that uses a team of LLMs and a quality control pipeline, and it outperforms state-of-the-art methods in empirical tests.
While tabular data is fundamental to many real-world machine learning (ML) applications, acquiring high-quality tabular data is usually labor-intensive and expensive. Limited by the scarcity of observations, tabular datasets often exhibit critical deficiencies, such as class imbalance, selection bias, and low fidelity. To address these challenges, building on recent advances in Large Language Models (LLMs), this paper introduces Team-then-Trim (T$^2$), a framework that synthesizes high-quality tabular data through a collaborative team of LLMs, followed by a rigorous three-stage plug-in data quality control (QC) pipeline. In T$^2$, tabular data generation is conceptualized as a manufacturing process: specialized LLMs, guided by domain knowledge, are tasked with generating different data components sequentially, and the resulting products, i.e., the synthetic data, are systematically evaluated across multiple dimensions of QC. Empirical results on both simulated and real-world datasets demonstrate that T$^2$ outperforms state-of-the-art methods in producing high-quality tabular data, highlighting its potential to support downstream models when direct data collection is practically infeasible.