Original Paper: https://arxiv.org/abs/2406.15126
By: Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang
Abstract:
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.
In the rapidly evolving field of deep learning, the quantity and quality of data are paramount. Engineers and data scientists often grapple with the high costs, data scarcity, and privacy concerns associated with obtaining high-quality real-world data. Enter Large Language Models (LLMs) – these powerful tools offer a promising solution by generating synthetic data. But how effective are they? Let's delve into a recent survey that organizes and evaluates the current landscape of LLM-driven synthetic data generation, curation, and evaluation.
The process of generating synthetic data using LLMs can be broadly categorized into two main strategies: prompt engineering and multi-step generation.