AI PERSONA: Towards Life-long Personalization of LLMs
This addresses the need for personalized AI assistance for users, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the problem of enabling large language models to continuously adapt to individual users' evolving profiles for personalized assistance, introducing a framework and benchmarks for life-long personalization.
In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community mainly focus on scaling data and compute for improved capabilities of LLMs, we argue that it is also very important to enable LLM systems, or language agents, to continuously adapt to the diverse and ever-changing profiles of every distinct user and provide up-to-date personalized assistance. We provide a clear task formulation and introduce a simple, general, effective, and scalable framework for life-long personalization of LLM systems and language agents. To facilitate future research on LLM personalization, we also introduce methods to synthesize realistic benchmarks and robust evaluation metrics. We will release all codes and data for building and benchmarking life-long personalized LLM systems.