mlsanityData to 7 Oct 2026

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

arXiv 2409.16973 ↗First posted 25 Sep 2024cs.CL cs.AI cs.LGcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 39 of its 50 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

39 of 50 references.

References · 39

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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27 more
  1. 2024Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoningcited by 41
  2. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  3. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendationcited by 27
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  5. 2023Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesiscited by 20
  6. 2024Real-Time Pill Identification for the Visually Impaired Using Deep Learningcited by 20
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  8. 2023Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real Worldcited by 18
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  16. 2022NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithmscited by 8
  17. 2022On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systemscited by 6
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  22. 2024Electroencephalogram Emotion Recognition via AUC Maximizationcited by 2
  23. 2023Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networkscited by 1
  24. 2023Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashingcited by 1
  25. 2023FREEDOM: Target Label & Source Data & Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalizationcited by 1
  26. 2023Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansioncited by 1
  27. 2024Leveraging Translation For Optimal Recall: Tailoring LLM Personalization With User Profilescited by 1

Built on by

Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skillscited by 19
  2. 2025LettinGo: Explore User Profile Generation for Recommendation Systemcited by 11
  3. 2025NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Searchcited by 4
  4. 2025Advances in Large Language Models for Medicinecited by 1
  5. 2025When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detectioncited by 1