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.

翻译：本文提出了大语言模型终身个性化的研究任务。当前LLM领域的主流研究主要集中于通过扩展数据和计算资源来提升模型能力，我们认为同样重要的是使LLM系统（或称语言智能体）能够持续适应每个独立用户多样化且不断变化的特征，并提供与时俱进的个性化服务。我们给出了清晰的任务定义，并提出了一种简单、通用、高效且可扩展的终身个性化框架。为促进LLM个性化研究的未来发展，我们还提出了合成真实基准数据集的方法以及鲁棒的评估指标。我们将开源构建和评估终身个性化LLM系统的全部代码与数据。