Fine-Tuning Private GPT For Better Performance And Data Privacy

In recent years, the advancements in natural language processing (NLP) facilitated by large-scale pre-trained models like GPT series have significantly improved various applications. However, concerns regarding user privacy and data security have arisen due to the centralized nature of model training, which often involves vast amounts of sensitive data. To address these concerns, private GPT model development aimed at preserving user privacy while maintaining the efficiency of NLP tasks. methods for protecting privacy, such as multiparty computation, differential privacy, and federated learning to train GPT models without direct access to sensitive data. By incorporating these techniques, private GPT model achieves competitive performance while ensuring data confidentiality and privacy compliance. Furthermore, a comprehensive evaluation of the proposed approach on various NLP benchmarks, demonstrating its effectiveness in preserving privacy without sacrificing utility.