# VizAbility: Enhancing Chart Accessibility with LLM-based Conversational Interaction

arXiv: https://arxiv.org/abs/2310.09611 · HTML: https://mlsanity.com/paper/2310.09611/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2023.

## Built on by (most cited first, 12 of 12)

- [Benchmarking Visual Language Models on Standardized Visualization Literacy Tests](https://mlsanity.com/paper/2503.16632/) (2025)
- [ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking](https://mlsanity.com/paper/2504.14406/) (2025)
- [The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models](https://mlsanity.com/paper/2508.09716/) (2025)
- [A Review on Large Language Models for Visual Analytics](https://mlsanity.com/paper/2503.15176/) (2025)
- [Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach](https://mlsanity.com/paper/2506.06175/) (2025)
- [Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientists](https://mlsanity.com/paper/2607.18514/) (2026)
- [GamerAstra: Supporting 2D Non-Twitch Video Games for Blind and Low-Vision Players through a Multi-Agent Framework](https://mlsanity.com/paper/2506.22937/) (2025)
- [SweeperBot: Making 3D Browsing Accessible through View Analysis and Visual Question Answering](https://mlsanity.com/paper/2511.14567/) (2025)
- [Lost in Instructions: Study of Blind Users' Experiences with DIY Manuals and AI-Rewritten Instructions for Assembly, Operation, and Troubleshooting of Tangible Products](https://mlsanity.com/paper/2602.18630/) (2026)
- [A Systematic Survey on Image Description Techniques for STEM Domains](https://mlsanity.com/paper/2607.21611/) (2026)
- [Chart Deception in Vision-Language Models: From Vulnerability to Mitigation](https://mlsanity.com/paper/2607.22600/) (2026)
- [Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualization](https://mlsanity.com/paper/2607.25021/) (2026)

## Abstract (arXiv)

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.