On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation

Analysis

Why This Paper Matters

This paper addresses a critical bottleneck in deep learning: the scarcity and quality limitations of real-world data. By leveraging Large Language Models (LLMs) for synthetic data generation, the field gains a scalable, data-centric solution. However, prior work lacked a unified framework, making it difficult to compare methods or identify systematic gaps. This paper fills that void by proposing a generic workflow that covers generation, curation, and evaluation, thus providing a structured lens for both researchers and practitioners.

The timing is significant: with LLMs becoming more capable and accessible, synthetic data is increasingly used in domains like NLP, code generation, and multimodal learning. Without a coherent organizational scheme, efforts remain fragmented. This paper's taxonomy helps consolidate knowledge and directs future work toward the most pressing challenges, such as data diversity, fidelity, and bias mitigation.

Technical Contributions

• Unified Workflow: The paper defines a three-stage pipeline—generation, curation, evaluation—that encapsulates the end-to-end process of using LLMs for synthetic data.
• Gap Analysis: By mapping existing studies onto this workflow, the authors identify underexplored areas, such as automated curation methods and robust evaluation metrics.
• Future Directions: The paper outlines specific research avenues, including improving controllability of generation, reducing artifacts, and developing domain-specific benchmarks.
• Cross-Domain Applicability: The framework is designed to be generic, applicable to text, code, and potentially other modalities.

Results

As a survey and position paper, no experimental results are reported. The main output is a structured organization of the literature and a set of identified research gaps. The paper does not provide quantitative metrics or comparisons between synthetic data generation methods.

Significance

This paper serves as a roadmap for the growing field of LLM-driven synthetic data. By providing a common vocabulary and workflow, it enables more systematic comparisons and collaborations. For industry practitioners, it offers a checklist for building robust synthetic data pipelines. For academics, it highlights open problems that could drive future research. The framework's generality means it can adapt as LLMs evolve, ensuring long-term relevance.