Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.16973 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909325812826112 |
|---|---|
| author | Mendoza, Rafael Cruz, Isabella Liu, Richard Deshmukh, Aarav Williams, David Peng, Jesscia Iyer, Rohan |
| author_facet | Mendoza, Rafael Cruz, Isabella Liu, Richard Deshmukh, Aarav Williams, David Peng, Jesscia Iyer, Rohan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16973 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization Mendoza, Rafael Cruz, Isabella Liu, Richard Deshmukh, Aarav Williams, David Peng, Jesscia Iyer, Rohan Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.16973 |