VizAbility: Enhancing Chart Accessibility with LLM-based Conversational Interaction
Name
3654777.3676414.pdf
Size
4.57 MB
Format
Adobe PDF
Checksum (MD5)
caee0134dbda5d682baa209bfb7c8923
Author(s)
Gorniak, Joshua
Kim, Yoon
Wei, Donglai
Kim, Nam Wook
Date Issued
October 13, 2024
Publisher
ACM|The 37th Annual ACM Symposium on User Interface Software and Technology
Citation
Gorniak, Joshua, Kim, Yoon, Wei, Donglai and Kim, Nam Wook. 2024. "VizAbility: Enhancing Chart Accessibility with LLM-based Conversational Interaction."
Version
Final published version
Abstract
Traditional accessibility methods like alternative text and data tables typically underrepresent data visualization’s full potential. Keyboard-based chart navigation has emerged as a potential solution, yet efficient data exploration remains challenging. We present VizAbility, a novel system that enriches chart content navigation with conversational interaction, enabling users to use natural language for querying visual data trends. VizAbility adapts to the user’s navigation context for improved response accuracy and facilitates verbal command-based chart navigation. Furthermore, it can address queries for contextual information, designed to address the needs of visually impaired users. We designed a large language model (LLM)-based pipeline to address these user queries, leveraging chart data & encoding, user context, and external web knowledge. We conducted both qualitative and quantitative studies to evaluate VizAbility’s multimodal approach. We discuss further opportunities based on the results, including improved benchmark testing, incorporation of vision models, and integration with visualization workflows.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3654777.3676414
