Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualization
Multimodal large language models (MLLMs) can connect visualization patterns to external causes, consequences, and domain knowledge, but the evidential basis of these interpretations is often unclear. We present an exploratory study of 102 visualizations from four sources, three MLLMs, and four input conditions that vary access to the image, accessible chart context (non-image artifacts such as data tables, captions, alt text, and screen-reader structures), and withheld-context framing. Across 1,224 descriptions, we analyze model-attributed DIRECT, DERIVED, and SPECULATIVE labels and conduct an automated audit of numeric agreement. Accessible chart context shifted Gemini and GPT toward DIR...
| Authors | Ishrat Jahan Eliza, Md Dilshadur Rahman |
|---|---|
| Primary category | cs.AI |
| Published | 27 July 2026 |
| Last updated at source | 24 September 2026 |
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