Enterprise AI Analysis
Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation
This paper introduces Team-then-Trim (T2), a novel 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. T² ensures synthetic data is not only plausible but also diverse and task-aligned, enhancing AI applications even with limited initial data.
Executive Impact: Revolutionizing Data Quality
In an era where high-quality tabular data is essential yet often scarce, T² provides a groundbreaking solution. By leveraging specialized LLMs in an assembly-line fashion and implementing a rigorous three-stage QC pipeline, T² ensures synthetic data is not only plausible but also diverse and task-aligned. This translates directly into improved downstream model performance and significant cost savings, making advanced AI applications accessible even with limited initial data.
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AUC Gain in HR Group
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AUC Gain in LR Group
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Diversity Gain (β-recall)
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Validity Gain (Detection)
Deep Analysis & Enterprise Applications
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Enterprise Process Flow: LLM Teaming for Data Generation
Task Manager Decomposes Task
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Worker LLMs Generate Components Sequentially
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Components Assembled
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Labels Generated by Final Worker LLM
Data Quality Control Pipeline
Sanity Check
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Objective-Related Cost Assessment
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Diversity Inspection
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Batch Accepted/Rejected
| Method | Imbalance (HR) | Incompleteness (HR) |
|---|---|---|
| Dori | 64.24 | 48.69 |
| SMOTE | 68.66 | 48.52 |
| TVAE | 65.22 | 49.85 |
| CTGAN | 67.00 | 49.87 |
| CLLM | 65.79 | 50.49 |
| EPIC | 75.33 | 50.28 |
| T² (ours) | 76.98 | 64.67 |
Key Finding: Robustness to Label Noise
Average AUC with moderate label noise (Flip Ratio 0.2)Key Finding: Downstream Utility on Real-World Data
Average AUC on COMPAS DatasetCase Study: Preventing Logical Inconsistencies with LLM Teaming
The LLM Teaming framework ensures adherence to domain constraints, unlike single LLMs. For instance, in the COMPAS dataset, 'priors_count' must always be >= 'juv_fel_count'. A single LLM often violates this, while T²'s collaborative, sequential generation preserves such inter-component logic. This structured approach significantly improves data fidelity and the reliability of synthetic data for sensitive applications.
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Annual Cost Savings
Hours Reclaimed Annually
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Phase 4: Optimization & Continuous Improvement
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