2025/04/24 by Yuhan Liu, Aadit Shah, Jordan Ackerman +1 · 1 voice · 1 citation
Computer Science · Decision Sciences · #Data Quality and Management #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services
paper · doi:10.1145/3706598.3714262
openalex publication_date 2025/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Knowledge gaps often arise during communication due to diverse backgrounds, knowledge bases, and vocabularies. With recent LLM developments, providing real-time knowledge support is increasingly viable, but is challenging due to shared and individual cognitive limitations (e.g., attention, memory, and comprehension) and the difficulty in understanding the user's context and internal knowledge. To address these challenges, we explore the key question of understanding how people want to receive real-time knowledge support. We built StopGap -- a prototype that provides real-time knowledge support for explaining jargon words in videos -- to conduct a design probe study (N=24) that explored multiple visual knowledge representation formats. Our study revealed individual differences in preferred representations and highlighted the importance of user agency, personalization, and mixed-initiative assistance. Based on our findings, we map out six key design dimensions for real-time LLM knowledge support systems and offer insights for future research in this space.