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RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models

Created by
  • Haebom

Author

Aditya Gunturu, Shivesh Jadon, Nandi Zhang, Morteza Faraji, Jarin Thundathil, Wesley Willett, Ryo Suzuki

Outline

This paper presents RealitySummary, a reading assistance system that integrates a mixed reality (MR) interface with a large-scale language model (LLM) to support everyday reading. RealitySummary seamlessly integrates always-on camera access, OCR-based text extraction, and augmented spatial and visual responses with the LLM. The study, based on user feedback and reflective analysis, involved three iterations, each evaluated through a user study (N=12), a field deployment (N=11), and a diary study (N=5). The experimental results highlight the unique advantages of combining AI and MR, including always-on implicit support, long-term temporal recording, minimal context switching, and spatial capabilities, demonstrating the potential of future LLM-MR interfaces that go beyond traditional screen-based interactions.

Takeaways, Limitations

Takeaways:
Demonstrates the utility of a reading support system through integration of LLM and MR interfaces.
Introducing new ways of interacting, including always-on implicit support, long-term temporal recording, minimal context switching, and spatial functionality.
Presenting the future development potential of the LLM-MR interface.
Limitations:
The number of study participants is relatively small (limited number of participants at each study stage).
Research results limited to a specific situation (everyday reading).
Further research is needed to determine generalizability across different types of LLM and MR interfaces.
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