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New VisCodeBench dataset reveals limitations in text-to-visualization systems

By PulseAugur Editorial · [1 sources] ·

A new benchmark dataset called VisCodeBench has been introduced to evaluate text-to-visualization systems, particularly for complex, real-world scenarios. The benchmark includes 2,672 samples across educational, scientific, and general audience domains. When tested, current systems like Codex, Claude Code, and LIDA showed that while basic execution is improving, no system can yet fully replicate reference visualizations, with data similarity scores ranging from 0.69 to 0.81. The study also highlighted that system design, including prompting and refinement pipelines, significantly impacts outcomes, and latency remains a practical challenge for these tools. AI

IMPACT Highlights areas for improvement in AI's ability to generate visualizations from text, impacting data analysis and user interaction tools.

RANK_REASON The cluster describes a new benchmark dataset for evaluating AI systems, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New VisCodeBench dataset reveals limitations in text-to-visualization systems

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The cluster describes a new benchmark dataset for evaluating AI systems, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hy Nguyen, Thomas James Elton, Ella Wilson, Andrew Reeson, Cecile Paris, Josiah Poon, Jonathan K. Kummerfeld ·

    VisCodeBench: a new benchmark dataset for Text-to-Vis reflecting real-world practice

    arXiv:2611.02353v1 Announce Type: new Abstract: Generating visualisations from natural language requests (text-to-vis) enables users to explore data more efficiently. While LLMs can complete simple visualisation requests, their performance on challenging ones is not well covered …

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