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About this paper
Nilay Yilmaz, Maitreya Patel, Yiran Lawrence Luo, Tejas Gokhale, Chitta Baral, Suren Jayasuriya, Yezhou Yang
Researchers from Arizona State University introduce VOILA, a new open-ended, dynamically generated benchmark for evaluating Multimodal Large Language Models (MLLMs) on complex visual analogical reasoning. Their findings indicate a substantial gap between human performance and MLLMs, which struggle significantly with applying identified relationships to generate novel visual outputs, especially when distractions are present.
Key insights
- Multimodal Large Language Models struggle profoundly with applying identified abstract relationships to novel visual contexts for inference and image generation.
- The ability of MLLMs to reason directly from visual information is substantially weaker compared to reasoning from pre-processed textual descriptions.
- Introducing 'distraction' elements in visual reasoning tasks significantly increases difficulty for MLLMs, highlighting their fragility in discerning relevant information.
Summary by alphaXiv.
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