Data Augmentation Using Large Language Models: Methods, Challenges, and Perspectives

doi:10.1007/978-3-032-13869-9_5

Data Augmentation Using Large Language Models: Methods, Challenges, and Perspectives

2026-01-01

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DOI：10.1007/978-3-032-13869-9_5

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Data augmentation plays a pivotal role in improving machine learning performance, especially when labeled data are limited. With the rapid advancement of Large Language Models (LLMs), their ability to generate high quality synthetic data has attracted significant attention. In this study, we examine the use of LLMs for data augmentation, particularly the Polish large-language model Bielik. We assess its proficiency in producing diverse and contextually relevant synthetic text to enrich datasets within the financial sector, using data sourced from a leasing company. Our investigation covers a variety of enhancement strategies. These include controlled text generation through paraphrasing (with adjustable prompt parameters), the addition of noise to continuous variables (with modifiable scaling), and binary variable modifications through flipping at predetermined rates. We evaluated the impact of these techniques on overall model performance while addressing challenges such as data quality, bias mitigation, and ethical considerations in integrating LLM generated data into machine learning workflows. Ultimately, this research provides forward looking perspectives on the evolving applications and potential enhancements of LLM driven data augmentation, offering valuable insights for both practitioners and researchers.

Keywords:

Data augmentation

Large Language Models

Synthetic data

Financial sector

Machine learning

Data Augmentation Using Large Language Models: Methods, Challenges, and Perspectives

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