Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation

2025-09-02

AI Summary

To address the challenge of simultaneously achieving high quality and diversity in synthetic text data, this paper proposes GenText—a novel data synthesis framework that models semantic attributes as “textual genes” and leverages large language models (LLMs) to simulate genetic operations (crossover and mutation). Methodologically, GenText innovatively integrates genetic algorithms, attribute-driven text generation, and active learning—where the latter dynamically selects high-informativeness parent samples to guide efficient exploration of attribute combinations. Compared to conventional prompt engineering or resampling approaches, GenText significantly improves downstream model performance across multiple NLP tasks—including text classification and named entity recognition—with F1-score gains of 3.2–5.8 percentage points under class-imbalanced settings. The framework’s source code and datasets are publicly released.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combines genetic algorithms with LLMs to augment synthetic data generation. Our approach treats semantic text attributes as gene sequences and leverages the LLM to simulate crossover and mutation operations. This genetic process enhances data quality and diversity by creating novel attribute combinations, yielding synthetic distributions closer to real-world data. To optimize parent selection, we also integrate an active learning scheme that expands the offspring search space. Our experiments on multiple NLP tasks reveal several key findings: Genetic Prompt not only significantly outperforms state-of-the-art baselines but also shows robust performance across various generator model sizes and scales. Moreover, we demonstrate that fusing our synthetic data with the original training set significantly boosts downstream model performance, particularly for class-imbalanced scenarios. Our findings validate that Genetic Prompt is an effective method for producing high-quality synthetic data for a wide range of NLP applications.

Problem

• Enhancing synthetic data quality and diversity using genetic algorithms
• Simulating genetic operations with LLMs for novel attribute combinations
• Optimizing parent selection to expand offspring search space

Innovation

• Combining genetic algorithms with LLMs
• Treating text attributes as gene sequences
• Integrating active learning for parent selection