Leveraging Large Language Models to Generate Training Data in Low-Resource Scenarios

Abstract

The persistent scarcity of training data continues to be a major roadblock for machine learning applications in specialized domains and underrepresented languages. This article explores how Large Language Models (LLMs) are emerging as a promising solution to this problem by serving as synthetic data generators. When human-annotated data is unavailable or too expensive to obtain, LLMs can produce labeled examples that bootstrap model development. This paper examines several methodological approaches to synthetic data generation—instruction tuning, few-shot prompting, and chain-of-thought techniques—and their applications in zero-shot and few-shot learning contexts. The article also investigates how LLMs can facilitate data augmentation, error case generation, and counterfactual testing to improve model robustness and fairness. Despite their potential, significant challenges remain, including bias amplification, distribution mismatch between synthetic and real data, quality assurance concerns, and computational demands. Looking ahead, the article identifies promising research directions in controlled generation, hybrid data approaches, adaptive generation systems, standardized quality metrics, and domain adaptation techniques that may fundamentally change how businesses develop machine learning systems for specialized applications and underrepresented domains.