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

2026-02-04

AI Summary

This work addresses the challenges of high-quality tabular data synthesis, including class imbalance, selection bias, and low fidelity. The authors propose a “team-based generation plus pruning” paradigm that models tabular synthesis as an assembly-line process: multiple domain-knowledge-guided specialized large language models collaboratively generate data components, which are then systematically evaluated and refined through a three-stage, plug-and-play quality control pipeline. This approach uniquely integrates multi-agent collaboration with structured quality assurance, significantly outperforming state-of-the-art methods on both synthetic and real-world datasets. The resulting synthetic data exhibit markedly improved fidelity and utility, effectively supporting downstream machine learning tasks.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Teamwork
• Humans and AI: Teamwork, Team formation

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• tabular data
• data quality
• class imbalance
• selection bias
• low fidelity

Innovation

• Large Language Models
• Tabular Data Generation
• Data Quality Control
• Collaborative LLM Framework
• Synthetic Data