Enterprise AI Analysis
TAGAL: Tabular Data Generation using Agentic LLM Methods
The generation of data is a common approach to improve the performance of machine learning tasks, among which is the training of models for classification. In this paper, we present TAGAL, a collection of methods able to generate synthetic tabular data using an agentic workflow. The methods leverage Large Language Models (LLMs) for an automatic and iterative process that uses feedback to improve the generated data without any further LLM training. The use of LLMs also allows for the addition of external knowledge in the generation process. We evaluate TAGAL across diverse datasets and different aspects of quality for the generated data. We look at the utility of downstream ML models, both by training classifiers on synthetic data only and by combining real and synthetic data. Moreover, we compare the similarities between the real and the generated data. We show that TAGAL is able to perform on par with state-of-the-art approaches that require LLM training and generally outperforms other training-free approaches. These findings highlight the potential of agentic workflow and open new directions for LLM-based data generation methods.
Executive Impact: Addressing Key Challenges with TAGAL
TAGAL directly addresses critical pain points in tabular data management and analysis, delivering significant improvements for enterprise operations:
Core Problems Addressed:
- ● Imbalanced or scarce tabular data, especially in sensitive domains like healthcare and finance.
- ● Difficulty in acquiring sufficient data for robust model training and domain representation.
- ● Privacy concerns hindering the sharing of sensitive tabular datasets.
Strategic Solutions Delivered:
- ● Synthetic data generation to overcome data scarcity and class imbalance effectively.
- ● Reduced costs and increased scalability for obtaining new, diverse examples.
- ● Privacy-preserving data sharing capabilities through advanced generative models.
- ● Agentic LLM workflows for automatic, iterative, and training-free data generation with integrated feedback.
0.98
Max Utility (U.TSTR)
3.16%
Min Collisions (%)
2
LLMs in Core Loop
Our research demonstrates how TAGAL's agentic LLM approach not only mitigates these problems but often enhances data utility beyond traditional methods, even in uncontaminated scenarios.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction & Motivation
LLM-based Generation
TAGAL Methods Explained
Experimental Insights
Key Findings
Conclusion
Tabular data is fundamental across many industries, yet often suffers from issues like scarcity, class imbalance, and privacy concerns. Traditional machine learning models struggle with such limitations. Synthetic data generation offers a powerful solution, enabling the creation of new, diverse examples, reducing acquisition costs, and preserving privacy. This paper introduces TAGAL as an innovative agentic LLM approach to tackle these challenges.
Large Language Models (LLMs) have demonstrated exceptional in-context learning capabilities, performing well even on tasks for which they weren't explicitly trained. This makes them suitable for tabular data tasks like classification and, crucially, generation. Prior methods include fine-tuning LLMs on tabular data (GReaT, Tabula) or training-free approaches like EPIC, which rely on few-shot examples. A key advantage of LLMs is their potential to leverage background knowledge, leading to more realistic synthetic data.
TAGAL comprises three agentic, training-free LLM-based methods: SynthLoop, ReducedLoop, and Prompt-Refine. All leverage an iterative feedback loop where one LLM generates data, and another provides critical feedback to improve subsequent generations. Prompt-Refine further introduces a third 'summary LLM' to refine the generation prompt itself, reducing token usage and potentially enhancing data diversity and quality.
TAGAL was evaluated on four diverse datasets (Adult, Bank, German, Thyroid) focusing on two key aspects: machine learning utility (TSTR and combined real/synthetic training) and similarity to real data (precision, recall, collisions). Thyroid dataset, being post-LLM training cutoff, provided a valuable test for contamination-free evaluation. Comparisons were made against state-of-the-art training-based (CTGAN, TabDDPM, GReaT, Tabula) and training-free (EPIC) methods, as well as a statistical baseline.
TAGAL methods often achieve utility comparable to training-based models and consistently outperform other training-free LLM approaches. The agentic feedback loop significantly reduces data collisions compared to methods like EPIC. Notably, on the Thyroid dataset (unlikely to be contaminated), TAGAL methods, especially Prompt-Refine, even improved upon the original data's classification utility. This highlights the power of agentic workflows and external knowledge.
0.98
Achieved Utility (U.TSTR) on Thyroid dataset, surpassing original data's 0.97.
Enterprise Process Flow: TAGAL's Agentic Loop
Initial Prompt
→
Generate Data (LLM)
→
Analyze Data (LLM)
→
Provide Feedback
→
Refine Generation (LLM)
| Feature | TAGAL (Agentic LLM) | Training-Based SOTA | Basic LLM Training-Free |
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| Iterative Feedback Loop |
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| External Knowledge Integration |
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| Automated Prompt Refinement |
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| Collision Reduction |
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| Data Scarcity Resilience |
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Thyroid Dataset: Uncontaminated Success
TAGAL's Prompt-Refine method achieved a U.TSTR score of 0.98 on the Thyroid dataset, compared to the original data's 0.97.
This finding is crucial as the Thyroid dataset was released after most current LLM training cutoffs, making data contamination unlikely. It demonstrates TAGAL's ability to leverage few-shot examples and prompt information to generate high-quality synthetic data, even improving upon original data utility in scenarios of limited or uncontaminated data. This validates the potential of agentic LLM workflows for future datasets and diverse domains.
TAGAL introduces a novel collection of agentic, training-free LLM methods for synthetic tabular data generation. By leveraging iterative feedback and prompt refinement, TAGAL achieves quality comparable to state-of-the-art training-based models and surpasses other training-free approaches. This work opens new avenues for LLM-based data generation, especially with its ability to integrate external knowledge and perform well with limited or uncontaminated data. Future work includes exploring additional datasets, conditional generation, and applications with extremely scarce data.
Calculate Your Potential AI ROI with TAGAL
Estimate the time and cost savings TAGAL could bring to your organization by optimizing data generation processes.
Your Enterprise AI Implementation Roadmap
A clear path to integrating TAGAL and unlocking the full potential of synthetic data in your organization.
Phase 1: Discovery & Strategy (2-4 Weeks)
Comprehensive analysis of your existing data generation challenges, current infrastructure, and specific business objectives. We'll identify key datasets for TAGAL application and define clear success metrics.
Phase 2: Pilot & Optimization (6-10 Weeks)
Implement TAGAL on a selected pilot dataset, generating initial synthetic data. We'll fine-tune prompts, evaluate data quality and utility, and demonstrate the agentic workflow's efficiency with iterative feedback loops.
Phase 3: Integration & Scaling (Ongoing)
Seamless integration of TAGAL into your existing data pipelines and ML workflows. We provide training for your teams and ongoing support to scale synthetic data generation across multiple applications and datasets, maximizing your ROI.
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