Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm
  Simulators for Conditional Synthetic Data Generation
Authors: name: Guangzeng Han, name: Weisi Liu, name: Xiaolei Huang

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.