Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm
  Simulators for Conditional Synthetic Data Generation
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.
68b7c566295f15ff609113e8
Guangzeng Han
admin_assigned
2025-09-03T09:06:25.084Z
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GUANGZENG HAN
user
kwangju
68b7c566295f15ff609113e9
Weisi Liu
68b7c566295f15ff609113ea
Xiaolei Huang
Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm
  Simulators for Conditional Synthetic Data Generation
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.
Genetic Prompt enhances synthetic data quality and diversity in NLP by combining genetic algorithms with LLMs, improving downstream model performance.
Genetic Prompt genetic algorithms LLMs semantic text attributes gene sequences crossover mutation active learning synthetic data generation NLP tasks class-imbalanced scenarios