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

arXiv: https://arxiv.org/abs/2409.16973 · HTML: https://mlsanity.com/paper/2409.16973/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 50 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 30 of 39)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Masked Autoencoders for Point Cloud Self-supervised Learning](https://mlsanity.com/paper/2203.06604/) (2022)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Can LLM be a Personalized Judge?](https://mlsanity.com/paper/2406.11657/) (2024)
- [Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks](https://mlsanity.com/paper/2303.17602/) (2023)
- [Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing](https://mlsanity.com/paper/2310.18653/) (2023)
- [Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.14170/) (2024)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation](https://mlsanity.com/paper/2312.11336/) (2023)
- [QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning](https://mlsanity.com/paper/2402.10462/) (2024)
- [Real-Time Pill Identification for the Visually Impaired Using Deep Learning](https://mlsanity.com/paper/2405.05983/) (2024)
- [Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis](https://mlsanity.com/paper/2311.12275/) (2023)
- [Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception](https://mlsanity.com/paper/2310.13712/) (2023)
- [Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World](https://mlsanity.com/paper/2310.10207/) (2023)
- [Robust Domain Generalization for Multi-modal Object Recognition](https://mlsanity.com/paper/2408.05831/) (2024)
- [Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis](https://mlsanity.com/paper/2303.02460/) (2023)
- [MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases](https://mlsanity.com/paper/2406.10290/) (2024)
- [Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model Reasoning](https://mlsanity.com/paper/2310.01446/) (2023)
- [Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimization](https://mlsanity.com/paper/2403.13703/) (2024)
- [Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research](https://mlsanity.com/paper/2403.14483/) (2024)
- [Evaluating the Fairness Impact of Differentially Private Synthetic Data](https://mlsanity.com/paper/2205.04321/) (2022)
- [NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms](https://mlsanity.com/paper/2205.01198/) (2022)
- [On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems](https://mlsanity.com/paper/2201.10382/) (2022)
- [Zero-Shot Stance Detection using Contextual Data Generation with LLMs](https://mlsanity.com/paper/2405.11637/) (2024)

## Built on by (most cited first, 5 of 5)

- [Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills](https://mlsanity.com/paper/2512.16301/) (2025)
- [LettinGo: Explore User Profile Generation for Recommendation System](https://mlsanity.com/paper/2506.18309/) (2025)
- [NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search](https://mlsanity.com/paper/2505.14680/) (2025)
- [Advances in Large Language Models for Medicine](https://mlsanity.com/paper/2509.18690/) (2025)
- [When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection](https://mlsanity.com/paper/2510.12476/) (2025)

## Abstract (arXiv)

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.