RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models
<|reference_start|>RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models: Large Language Models (LLMs) are gaining popularity as tools for reading and summarization aids. However, little is known about their potential benefits when integrated with mixed reality (MR) interfaces to support everyday reading assistants. We developed RealitySummary, an MR reading assistant that seamlessly integrates LLMs with always-on camera access, OCR-based text extraction, and augmented spatial and visual responses in MR interfaces. Developed iteratively, RealitySummary evolved across three versions, each shaped by user feedback and reflective analysis: 1) a preliminary user study to understand user perceptions (N=12), 2) an in-the-wild deployment to explore real-world usage (N=11), and 3) a diary study to capture insights from real-world work contexts (N=5). Our findings highlight the unique advantages of combining AI and MR, including an always-on implicit assistant, minimal context switching, and spatial affordances, demonstrating significant potential for future LLM-MR interfaces beyond traditional screen-based interactions.<|reference_end|>
@article{gunturu2024realitysummary:,
    title={RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and
  Question Answering using Large Language Models},
    author={Aditya Gunturu, Shivesh Jadon, Nandi Zhang, Morteza Faraji, Jarin
  Thundathil, Tafreed Ahmad, Wesley Willett, Ryo Suzuki},
    journal={arXiv preprint arXiv:2405.18620},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18620},
    primaryClass={cs.HC cs.AI cs.CL}
}