团队后再修剪：一种用于高质量表格数据生成的流水线LLM框架
Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation
AI总结：
Team-then-Trim框架通过协作LLMs生成高质量表格数据，并结合三阶段质量控制流程，有效解决表格数据稀缺和质量低的问题。
摘要：
尽管表格数据对许多现实世界的机器学习（ML）应用至关重要，但获取高质量的表格数据通常是劳动密集型且昂贵的。受限于观测的稀缺性，表格数据集往往表现出关键缺陷，如类别不平衡、选择偏差和低保真度。为了解决这些挑战，基于最近在大型语言模型（LLMs）中的进展，本文引入了Team-then-Trim（T$^2$）框架，该框架通过一组协作的LLMs合成高质量的表格数据，随后通过严格的三阶段插件数据质量控制（QC）流程进行处理。在T$^2$中，表格数据生成被概念化为制造过程：专门的LLMs在领域知识的指导下，依次生成不同的数据组件，生成的结果，即合成数据，会系统地在多个质量控制（QC）维度上进行评估。在模拟和真实世界数据集上的实证结果表明，T$^2$在生成高质量表格数据方面优于最先进的方法，突显了其在直接数据收集不可行时支持下游模型的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
