Prompt Public Large Language Models to Synthesize Data for Private On-device Applications

arXiv:2404.04360v1 Announce Type: new
Abstract: Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language models (LLMs) trained on public data can improve the quality of pre-training data for the on-device language models trained with DP and FL. We carefully design LLM prompts to filter and transform existing public data, and generate new data to resemble the real user data distribution. The model pre-trained …

abstract applications arxiv cs.cl cs.cr cs.lg data differential differential privacy federated learning language language models large language large language models llms paper performance pre-training privacy prompt public public data quality training training data type