Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization

TL;DR

This work tackles the challenge of tailoring large language models to individual users directly on devices without relying on extensive labeled data. It introduces Adaptive Self-Supervised Learning Strategies (ASLS), a dual-layer framework consisting of a user profiling layer and a neural adaptation layer that continuously fine-tunes the model in real time using self-supervised signals. Across multiple datasets and baselines, ASLS demonstrates superior user engagement and satisfaction, with ablation analyses confirming the necessity of both profiling and adaptation components as well as real-time feedback. The approach promises efficient, context-aware on-device personalization, reducing computational overhead while maintaining responsiveness in dynamic user environments.

Abstract

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.