Abstract:
The study illustrates how natural language processing (NLP) might prioritize improved privacy, especially when it comes to text-based evaluations of social media sentiments. The proposed concept suggests leveraging Language Models (LLMs), like GPT-3, to incorporate additional user data while ensuring privacy. Formal privacy measures such as differential privacy take precedence over traditional data access restrictions, providing users with assurances of data protection. Case studies on detecting emotional states in social media posts demonstrate that LLMs maintain strict privacy rules while utilizing more data, maintaining a 92% accuracy rate. Adding user context, such as social network and demographic information, could further improve model performance without compromising privacy. This approach diverges from conventional limitations on data access in NLP and emphasizes the importance of privacy-preserving LLMs for optimizing large datasets. The article concludes by recommending further research into integrating user circumstances into models while maintaining privacy protection.
