Enabling On-Device LLMs Personalization with Smartphone Sensing
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909266404704256 |
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| author | Zhang, Shiquan Ma, Ying Fang, Le Jia, Hong D'Alfonso, Simon Kostakos, Vassilis |
| author_facet | Zhang, Shiquan Ma, Ying Fang, Le Jia, Hong D'Alfonso, Simon Kostakos, Vassilis |
| contents | This demo presents a novel end-to-end framework that combines on-device large language models (LLMs) with smartphone sensing technologies to achieve context-aware and personalized services. The framework addresses critical limitations of current personalization solutions via cloud LLMs, such as privacy concerns, latency and cost, and limited personal information. To achieve this, we innovatively proposed deploying LLMs on smartphones with multimodal sensor data through context-aware sensing and customized prompt engineering, ensuring privacy and enhancing personalization performance. A case study involving a university student demonstrated the capability of the framework to provide tailored recommendations. In addition, we show that the framework achieves the best trade-off in privacy, performance, latency, cost, battery and energy consumption between on-device and cloud LLMs. To the best of our knowledge, this is the first framework to provide on-device LLMs personalization with smartphone sensing. Future work will incorporate more diverse sensor data and involve extensive user studies to enhance personalization. Our proposed framework has the potential to substantially improve user experiences across domains including healthcare, productivity, and entertainment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling On-Device LLMs Personalization with Smartphone Sensing Zhang, Shiquan Ma, Ying Fang, Le Jia, Hong D'Alfonso, Simon Kostakos, Vassilis Human-Computer Interaction Artificial Intelligence Machine Learning This demo presents a novel end-to-end framework that combines on-device large language models (LLMs) with smartphone sensing technologies to achieve context-aware and personalized services. The framework addresses critical limitations of current personalization solutions via cloud LLMs, such as privacy concerns, latency and cost, and limited personal information. To achieve this, we innovatively proposed deploying LLMs on smartphones with multimodal sensor data through context-aware sensing and customized prompt engineering, ensuring privacy and enhancing personalization performance. A case study involving a university student demonstrated the capability of the framework to provide tailored recommendations. In addition, we show that the framework achieves the best trade-off in privacy, performance, latency, cost, battery and energy consumption between on-device and cloud LLMs. To the best of our knowledge, this is the first framework to provide on-device LLMs personalization with smartphone sensing. Future work will incorporate more diverse sensor data and involve extensive user studies to enhance personalization. Our proposed framework has the potential to substantially improve user experiences across domains including healthcare, productivity, and entertainment. |
| title | Enabling On-Device LLMs Personalization with Smartphone Sensing |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.04418 |