IGenBench: New Benchmark Reveals Significant Reliability Issues in AI-Generated Infographics
Researchers have created IGenBench, the first benchmark for evaluating how reliably text-to-image AI models generate infographics, testing 10 state-of-the-art models on 600 curated test cases. The study found that while top models achieve 90% accuracy on individual elements, they only reach 49% accuracy when all components must be correct together. Data-related issues emerged as a universal weakness across all models, with data completeness scoring only 21% accuracy.
Researchers have introduced IGenBench, a comprehensive benchmark designed to assess the reliability of text-to-infographic generation by AI models. The benchmark comprises 600 curated test cases spanning 30 different infographic types and uses an automated evaluation framework that breaks down reliability verification into atomic yes/no questions across 10 question categories. The study evaluated 10 state-of-the-art text-to-image models and found a significant gap between component-level accuracy (Q-ACC of 0.90 for the top model) and end-to-end infographic accuracy (I-ACC of only 0.49), revealing that models struggle with achieving correctness across all dimensions simultaneously. Data-related dimensions consistently emerged as universal bottlenecks across all models, with data completeness achieving only 0.21 accuracy. The researchers highlight that generated infographics may appear visually correct at first glance but contain subtle errors such as distorted data encoding or incorrect textual content that are easily overlooked. The benchmark and evaluation framework are publicly released to support future model development.
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- arXiv cs.LGCenter
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation
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