ICML spotlight | 一种会「进化」的合成数据！无需上传隐私，也能生成高质量垂域数据

Core Viewpoint - The article discusses the challenges of data scarcity in the context of large models and introduces the PCEvolve framework, which aims to generate synthetic datasets while preserving privacy and addressing the specific needs of vertical domains such as healthcare and industrial manufacturing [1][2][10]. Group 1: Data Scarcity and Challenges - The rapid development of large models has exacerbated the issue of data scarcity, with predictions indicating that public data generation will not keep pace with the consumption rate required for training these models by 2028 [1]. - In specialized fields like healthcare and industrial manufacturing, the availability of data is already limited, making the data scarcity problem even more severe [1]. Group 2: PCEvolve Framework - PCEvolve is a synthetic data evolution framework that requires only a small number of labeled samples to generate an entire dataset while protecting privacy [2]. - The evolution process of PCEvolve is likened to DeepMind's FunSearch and AlphaEvolve, focusing on generating high-quality training data from existing large model APIs [2]. Group 3: Limitations of Existing Large Models - Existing large model APIs cannot directly synthesize domain-specific data, as they fail to account for various characteristics unique to vertical domains, such as lighting conditions, sampling device models, and privacy information [4][7]. - The inability to upload local data due to privacy and intellectual property concerns complicates the prompt engineering process and reduces the quality of synthetic data [9][11]. Group 4: PCEvolve's Mechanism - PCEvolve employs a new privacy protection method based on the Exponential Mechanism, which is designed to adapt to the limited sample situation in vertical domains [11]. - The framework includes an iterative evolution process where a large number of candidate synthetic data are generated, followed by a selection process that eliminates lower-quality data based on privacy-protected scoring [11][19]. Group 5: Experimental Results - PCEvolve's effectiveness was evaluated through two main approaches: the impact of synthetic data on downstream model training and the quality of the synthetic data itself [21]. - In experiments involving datasets such as COVIDx and Came17, PCEvolve demonstrated significant improvements in model accuracy, with the final accuracy for COVIDx reaching 64.04% and for Came17 reaching 69.10% [22][23].