From Gap to Synergy: Enhancing Contextual Understanding through Human-Machine Collaboration in Personalized Systems

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Abstract

This paper presents LangAware, a collaborative approach for constructing personalized context for context-aware applications. The need for personalization arises due to significant variations in context between individuals based on scenarios, devices, and preferences. However, there is often a notable gap between humans and machines in the understanding of how contexts are constructed, as observed in trigger-action programming studies such as IFTTT. LangAware enables end-users to participate in establishing contextual rules in-situ using natural language. The system leverages large language models (LLMs) to semantically connect low-level sensor detectors to high-level contexts and provide understandable natural language feedback for effective user involvement. We conducted a user study with 16 participants in real-life settings, which revealed an average success rate of 87.50% for defining contextual rules in a variety of 12 campus scenarios, typically accomplished within just two modifications. Furthermore, users reported a better understanding of the machine's capabilities by interacting with LangAware.

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APA

Chen, W., Yu, C., Wang, H., Wang, Z., Yang, L., Wang, Y., … Shi, Y. (2023). From Gap to Synergy: Enhancing Contextual Understanding through Human-Machine Collaboration in Personalized Systems. In UIST 2023 - Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. Association for Computing Machinery, Inc. https://doi.org/10.1145/3586183.3606741

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