Personalized Real-time Jargon Support for Online Meetings
Yifan Song, Wing Yee Au, Hon Yung Wong, Brian P. Bailey, Tal August

TL;DR
This paper introduces ParseJargon, an LLM-powered system that provides real-time personalized jargon explanations during online meetings, significantly improving comprehension and engagement for interdisciplinary communication.
Contribution
The paper presents a novel personalized jargon support system, ParseJargon, and demonstrates its effectiveness through controlled and field studies in real-time online meetings.
Findings
Personalized support improves comprehension and engagement.
General-purpose support can negatively affect engagement.
ParseJargon is usable and valuable in real-world settings.
Abstract
Effective interdisciplinary communication is frequently hindered by domain-specific jargon. To explore the jargon barriers in-depth, we conducted a formative diary study with 16 professionals, revealing critical limitations in current jargon-management strategies during workplace meetings. Based on these insights, we designed ParseJargon, an interactive LLM-powered system providing real-time personalized jargon identification and explanations tailored to users' individual backgrounds. A controlled experiment comparing ParseJargon against baseline (no support) and general-purpose (non-personalized) conditions demonstrated that personalized jargon support significantly enhanced participants' comprehension, engagement, and appreciation of colleagues' work, whereas general-purpose support negatively affected engagement. A follow-up field study validated ParseJargon's usability and practical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
