Fine-Tuning LLMs for Personality Preservation in AI Assistants

The application of large language models (LLMs) in artificial intelligence (AI) assistants has drawn a lot of interest due to their ability to generate conversations that are close to human-like interaction and to provide contextually relevant responses. Nonetheless, the age-old problem is still that of preserving the personality and consistency of the AI system across different interactions. Despite the advances in conversational AI, most AI assistants lack the ability to maintain a consistent personality, resorting to generating robotic conversations or those that lack personality. This work aims to close the personality conservation gap in AI assistants through the exploration of methods to fine-tune LLMs for consistent personality preservation. Through data-driven methods, the research explores the role of user selection, context recall, and user-specific interaction history in shaping and preserving the personality of an AI assistant. Additionally, the research explores the application of emotional intelligence and adaptive learning algorithms to make the assistant persona more natural, dynamic, and user-relevant. This work introduces a new paradigm for LLM fine-tuning that is able to leverage such factors while allowing responsiveness, adaptability, and user appeal. The ultimate contribution of this research is to lay the foundation for the creation of AI assistants that can provide personalized experiences without compromising reliability or user enjoyment. The expectation is that findings will have far-reaching implications in customer service, personal assistance, and other areas where consistent and engaging AI personalities are the secret to successful user interaction.