On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

By Lin Long, Rui Wang, Rui Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang. Annual Meeting of the Association for Computational Linguistics, 2024

343 citations recorded by the source corpus.

Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.

Subfields: Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/ffeb4c933b81f06724379603e69e1b8f9cfbdb64

Citations in the corpus

• WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (2023, neutral): “However, these open models still struggle with the scenarios which require complex multi-step quantitative reasoning, such as solving mathematical and science challenges (Ahn et al., 2024; Long et al., 2024).”