arXiv:2601.04498v2 Announce Type: replace Abstract: Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information. While recent text-to-image (T2I) models can generate aesthetically appealing images, their reliability in generating infographics remains unclear. Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content. We present IGENBENCH, the first benchmark for evaluating the reliability of text-to-infographic gen
The proliferation of advanced text-to-image models necessitates robust benchmarking to ensure their utility beyond aesthetics, specifically for critical information communication like infographics.
This benchmark directly addresses the reliability gap in generative AI, which directly impacts trust and adoption of AI-generated content in professional and critical applications.
The focus shifts from merely generating visually appealing AI content to ensuring its factual accuracy and data integrity, pushing generative AI towards more rigorous validation.
- · AI Safety Researchers
- · Generative AI Developers
- · Data Journalists
- · Information Design Software Companies
- · Overly Optimistic AI Implementers
- · Low-quality AI Content Producers
Increased emphasis on fact-checking and validation tools for AI-generated visual content.
Development of regulatory standards or best practices for the use of AI in information communication.
Reduced spread of AI-generated misinformation through visual channels as reliability becomes a core metric.
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Read at arXiv cs.LG