ChainStream: An LLM-based Framework for Unified Synthetic Sensing
Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-concerning for end-users. In this paper, we propose to use natural language as the unified interface to process personal da...
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Zusammenfassung: | Many applications demand context sensing to offer personalized and timely
services. Yet, developing sensing programs can be challenging for developers
and using them is privacy-concerning for end-users. In this paper, we propose
to use natural language as the unified interface to process personal data and
sense user context, which can effectively ease app development and make the
data pipeline more transparent. Our work is inspired by large language models
(LLMs) and other generative models, while directly applying them does not solve
the problem - letting the model directly process the data cannot handle complex
sensing requests and letting the model write the data processing program
suffers error-prone code generation. We address the problem with 1) a unified
data processing framework that makes context-sensing programs simpler and 2) a
feedback-guided query optimizer that makes data query more informative. To
evaluate the performance of natural language-based context sensing, we create a
benchmark that contains 133 context sensing tasks. Extensive evaluation has
shown that our approach is able to automatically solve the context-sensing
tasks efficiently and precisely. The code is opensourced at
https://github.com/MobileLLM/ChainStream. |
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DOI: | 10.48550/arxiv.2412.15240 |