Efficient Synthetic Tabular Data Generation using Group-wise Prompting with Large Language Models

Core Concepts

A novel group-wise prompting method that leverages the in-context learning capabilities of large language models to efficiently generate high-quality synthetic tabular data, addressing challenges such as data imbalance and preserving inter-feature correlations.

Abstract

The study introduces a simple yet effective method for generating realistic synthetic tabular data using large language models (LLMs). The key aspects of the proposed approach are:

Efficient Tabular Data Formatting in Prompts:

Minimal preprocessing of the original data, maintaining the integrity of the raw data.
Use of a CSV-style format within the prompt to optimize token usage and enable the LLM to better recognize and preserve feature correlations.

Random Word Replacement Strategy:

Replacing categorical variable values with unique alphanumeric strings to introduce diversity and mitigate the issue of monotonous data patterns.
This strategy helps the LLM better identify patterns and generate more diverse samples.

Group-wise Prompting Method:

Structuring the prompt in a coherent and predictable manner, with predefined groups of data samples sharing specific attributes.
This approach guides the LLM to generate data that faithfully represents the original dataset under the specified conditions, ensuring consistent and condition-aligned outputs.

The effectiveness of the proposed method is extensively validated across eight real-world public datasets, demonstrating state-of-the-art performance in downstream classification and regression tasks. The method significantly improves the sensitivity of machine learning models, particularly on imbalanced datasets, without compromising the accuracy of majority classes. This advancement contributes to addressing key challenges in machine learning applications, particularly in the context of tabular data generation and handling class imbalance.

Stats

Generating realistic synthetic tabular data is a critical challenge in machine learning.
Obtaining large volumes of high-quality tabular data is often hindered by concerns such as cost, time, and privacy.
Real-world tabular data frequently exhibits scarcity and imbalance in features and classes, leading to skewed and underperforming machine learning models.

Quotes

"Generating realistic synthetic tabular data presents a critical challenge in machine learning."
"Our proposed random word replacement strategy significantly improves the handling of monotonous categorical values, enhancing the accuracy and representativeness of the synthetic data."
"The effectiveness of our method is extensively validated across eight real-world public datasets, achieving state-of-the-art performance in downstream classification and regression tasks while maintaining inter-feature correlations and improving token efficiency over existing approaches."

Key Insights Distilled From

Group-wise Prompting for Synthetic Tabular Data Generation using Large Language Models

by Jinhee Kim,T... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12404.pdf

Deeper Inquiries

How can the group-wise prompting method be extended to handle more complex data structures, such as hierarchical or time-series data?

The group-wise prompting method can be extended to handle more complex data structures by incorporating hierarchical or time-series data structures into the prompt design. For hierarchical data, the method can be adapted to include nested groups within the prompt, where each group represents a different level of the hierarchy. By organizing the data in a hierarchical fashion within the prompt, the LLM can learn the relationships and dependencies between different levels of the hierarchy, enabling the generation of synthetic data that preserves the hierarchical structure of the original data.
Similarly, for time-series data, the group-wise prompting method can be modified to include temporal information in the prompts. This can be achieved by structuring the prompts to include sequential data points representing different time steps. By providing examples of data points at different time intervals within each group, the LLM can learn the temporal dependencies and patterns present in the time-series data, facilitating the generation of synthetic time-series data that captures the temporal dynamics of the original dataset.
In essence, extending the group-wise prompting method to handle more complex data structures involves designing prompts that reflect the specific characteristics and relationships inherent in hierarchical or time-series data, allowing the LLM to learn and generate synthetic data that aligns with the complex structure of the original dataset.

What are the potential limitations of the random word replacement strategy, and how could it be further improved to preserve the semantic meaning of categorical variables?

While the random word replacement strategy is effective in introducing variability and diversity into categorical variables, there are potential limitations to consider. One limitation is the loss of semantic meaning associated with the original categorical values when replaced with random alphanumeric strings. This can impact the interpretability of the generated data and may lead to confusion when analyzing the synthetic dataset.
To address this limitation and improve the random word replacement strategy, several enhancements can be implemented:

Semantic Mapping: Instead of using purely random alphanumeric strings, a mapping table can be created to associate each original categorical value with a specific replacement string. This mapping can be based on semantic similarities or clustering of categorical values to ensure that the replacement strings retain some semantic relevance to the original values.

Contextual Embeddings: Utilizing contextual embeddings or pre-trained word embeddings can help capture the semantic relationships between categorical values. By embedding the original categorical values into a continuous vector space, similar values can be replaced with similar embeddings, preserving semantic meaning during the replacement process.

Hybrid Approach: Combining random word replacement with semantic mapping or contextual embeddings can offer a balanced approach. Random replacement can introduce variability, while semantic mapping or embeddings can ensure that the replacements maintain some level of semantic coherence with the original values.

By incorporating these enhancements, the random word replacement strategy can be refined to better preserve the semantic meaning of categorical variables in the synthetic data, enhancing the interpretability and utility of the generated dataset.

Given the success of the proposed method in tabular data generation, how could it be adapted to generate synthetic data for other modalities, such as images or text, while maintaining the core principles of the approach?

Adapting the proposed method for tabular data generation to other modalities like images or text involves translating the core principles of the approach to suit the characteristics of these data types. Here are some strategies to adapt the method for generating synthetic data in different modalities:

Image Data:

Structured Prompting: Design prompts that represent image features in a structured format, such as pixel values or image metadata. Group-wise prompting can be applied by organizing image samples based on visual attributes or categories.
Random Pixel Replacement: Similar to random word replacement, introduce variability in image data by randomly replacing pixels or image patches. This can help generate diverse image samples while preserving the overall visual content.

Text Data:

Token-Level Prompting: Structure prompts to represent text data at the token level, incorporating word sequences or sentence structures. Group-wise prompting can be used to generate text samples based on specific linguistic patterns or topics.
Random Word Substitution: Instead of replacing entire words, randomly substitute individual words or tokens in the text data. This can introduce variability in the text while maintaining syntactic and semantic coherence.

Hybrid Models:

Multi-Modal Generation: Explore the integration of multi-modal models that can handle different data types simultaneously. By combining tabular, image, and text generation components, a unified model can generate synthetic data across modalities while leveraging the strengths of each data type.

By adapting the core principles of the proposed method, such as structured prompting, variability introduction, and in-context learning, to the specific characteristics of images or text data, it is possible to extend the approach successfully to generate synthetic data in diverse modalities while maintaining its effectiveness and efficiency.