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

TL;DR

This paper introduces Adaptive Self-Supervised Learning Strategies (ASLS) for real-time, personalized on-device large language models, reducing resource use while improving user-specific response quality.

Contribution

The paper presents a novel self-supervised framework with real-time adaptation for personalized on-device LLMs, addressing resource constraints and enhancing responsiveness.

Findings

ASLS improves user engagement and satisfaction.

The approach reduces computational demands.

ASLS outperforms traditional personalization methods.

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.