Synthetic Data Generation Using LLMs

Analysis

Why This Paper Matters

This survey addresses a critical bottleneck in AI development: the scarcity of high-quality labeled data. As LLMs become more capable, their ability to generate synthetic training data offers a promising solution to augment or replace real-world datasets, especially in low-resource and sensitive domains. The paper provides a structured overview of the field, making it a valuable resource for practitioners seeking to leverage synthetic data.

By focusing on both natural language and code, the survey highlights the unique challenges and opportunities in each domain. In code, functional correctness can be automatically verified, enabling more reliable synthetic data generation. In natural language, issues like factual accuracy and bias are more prominent, requiring careful mitigation. This dual perspective is essential for understanding the full landscape of synthetic data generation.

Technical Contributions

• Categorization of generation techniques: The survey identifies three main approaches: prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. This taxonomy helps practitioners choose appropriate methods.
• Application mapping: It details how synthetic data can enrich low-resource tasks such as classification and question answering, and facilitate code-centric applications like instruction tuning, code translation, and bug repair.
• Mitigation strategies: The paper discusses filtering, weighting, and reinforcement learning with execution feedback as ways to address challenges like factual inaccuracies and bias amplification.
• Open research directions: It outlines future work in automated prompt engineering, cross-modal data synthesis, and robust evaluation frameworks, guiding ongoing research.

Results

As a survey, the paper does not present new experimental results. Instead, it synthesizes findings from the literature, noting that LLM-generated synthetic data can be cost-effective, provide broad coverage, and allow controllable diversity. However, it also highlights risks such as factual inaccuracies, insufficient stylistic realism, and bias amplification. The paper emphasizes that mitigation strategies, including filtering and reinforcement learning, are effective but require further development.

Significance

The survey underscores the transformative potential of LLM-generated synthetic data in accelerating AI development. By providing a comprehensive overview, it helps researchers and practitioners understand current capabilities and limitations, fostering more responsible adoption. The emphasis on ethical and quality safeguards is particularly important as synthetic data becomes more prevalent. This work will likely influence future research directions and practical applications, making it a key reference in the field.