Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications. Traditional methods often depend heavily on labeled datasets and can be resource-intensive. To address these issues, we present Adaptive Self-Supervised Learning Strategies (ASLS), which utilizes self-supervised learning techniques to personalize LLMs dynamically. The framework comprises a user profiling layer for collecting interaction data and a neural adaptation layer for real-time model fine-tuning. This innovative approach enables continuous learning from user feedback, allowing the model to generate responses that align closely with user-specific contexts. The adaptive mechanisms of ASLS minimize computational demands and enhance personalization efficiency. Experimental results across various user scenarios illustrate the superior performance of ASLS in boosting user engagement and satisfaction, highlighting its potential to redefine LLMs as highly responsive and context-aware systems on-device.

翻译：大型语言模型（LLM）彻底改变了我们与技术交互的方式，但根据个体用户偏好对其进行个性化定制仍是一个重大挑战，尤其是在设备端应用中。传统方法通常严重依赖标注数据集，且可能消耗大量资源。为解决这些问题，我们提出了自适应自监督学习策略（ASLS），该策略利用自监督学习技术动态地个性化LLM。该框架包含一个用于收集交互数据的用户画像层和一个用于实时模型微调的神经适应层。这种创新方法能够从用户反馈中持续学习，使模型能够生成与用户特定情境紧密契合的响应。ASLS的自适应机制最大限度地降低了计算需求，并提升了个性化效率。在不同用户场景下的实验结果表明，ASLS在提升用户参与度和满意度方面具有卓越性能，凸显了其将LLM重新定义为高度响应且具备情境感知能力的设备端系统的潜力。