让手机上的大模型实时学习用户习惯，不依赖标注数据。
Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization
- 用自监督学习动态更新本地模型，边用边学。
- 实测提升用户参与度与满意度，计算开销小。
- 适合注重隐私、需个性化响应的移动端应用。
大型语言模型（LLMs）革新了人机交互方式，但其在本地设备上个性化适配用户偏好仍面临挑战。传统方法高度依赖标注数据且资源消耗大。为此，我们提出自适应自监督学习策略（ASLS），利用自监督学习技术实现动态个性化。该框架包含用户画像层以收集交互数据，以及神经适应层实现实时模型微调。此方法能持续从用户反馈中学习，使模型生成更契合用户特定上下文的回复。ASLS的自适应机制显著降低计算开销，提升个性化效率。在多种用户场景下的实验结果表明，该方法在提升用户参与度和满意度方面表现优异，展现了将本地LLM重塑为高度响应且上下文感知系统的潜力。
原文摘要 · Abstract (English)
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.
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