# Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings

arXiv: https://arxiv.org/abs/2508.10239 · HTML: https://mlsanity.com/paper/2508.10239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 79 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Automated Lay Language Summarization of Biomedical Scientific Reviews](https://mlsanity.com/paper/2012.12573/) (2020)
- [Generative and Malleable User Interfaces with Generative and Evolving Task-Driven Data Model](https://mlsanity.com/paper/2503.04084/) (2025)
- [Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook](https://mlsanity.com/paper/2301.12243/) (2023)
- [What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning](https://mlsanity.com/paper/1811.12181/) (2018)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency](https://mlsanity.com/paper/1911.01678/) (2019)
- [Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications](https://mlsanity.com/paper/2212.09676/) (2022)
- [Know Your Audience: The benefits and pitfalls of generating plain language summaries beyond the "general" audience](https://mlsanity.com/paper/2403.04979/) (2024)
- [AutoMeTS: The Autocomplete for Medical Text Simplification](https://mlsanity.com/paper/2010.10573/) (2020)
- [One Size Does Not Fit All: The Case for Personalised Word Complexity Models](https://mlsanity.com/paper/2205.02564/) (2022)
- [RealityTalk: Real-Time Speech-Driven Augmented Presentation for AR Live Storytelling](https://mlsanity.com/paper/2208.06350/) (2022)
- [Hierarchical Summarization for Longform Spoken Dialog](https://mlsanity.com/paper/2108.09597/) (2021)
- [MadDog: A Web-based System for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2101.09893/) (2021)
- [Qlarify: Recursively Expandable Abstracts for Directed Information Retrieval over Scientific Papers](https://mlsanity.com/paper/2310.07581/) (2023)
- [Steering AI-Driven Personalization of Scientific Text for General Audiences](https://mlsanity.com/paper/2411.09969/) (2024)
- [Exploring the Design Space of Real-time LLM Knowledge Support Systems: A Case Study of Jargon Explanations](https://mlsanity.com/paper/2503.00715/) (2025)
- [Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work](https://mlsanity.com/paper/2503.18471/) (2025)

## Abstract (arXiv)

Cross-disciplinary communication is often hindered by specialized language (i.e., jargon) and uneven background knowledge. Recent advances in speech-to-text and large language models make it possible to provide jargon support during online meetings, but generic support (i.e., defining the same terms for everyone) can overwhelm listeners with definitions they do not need. We present ParseJargon, a system for personalized jargon support in real-time online meetings. We begin with an initial prototype to probe the use of single-sentence user profiles for personalization. We conducted a controlled study and showed that even this minimal personalization enhanced listeners' comprehension and engagement over generic support because of more precise jargon identification. Guided by insights from participants' feedback, we refined the system with more advanced personalization techniques, including in-session user feedback and portable glossary-based profiles. We evaluated how these techniques can further improve jargon identification precision using data collected in the controlled study to simulate personalization over time. We also conducted a latency test, complemented by a lightweight deployment, to analyze the system's real-time capability and usability.