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A Surprising Failure? Multimodal LLMs and the NLVR Challenge
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This study evaluates three state-of-the-art MLLMs -- GPT-4V, Gemini Pro, and the open-source model IDEFICS -- on the compositional natural language vision reasoning task NLVR. Given a human-written sentence paired with a synthetic image, this task requires the model to determine the truth value of the sentence with respect to the image. Despite the strong performance demonstrated by these models, we observe they perform poorly on NLVR, which was constructed to require compositional and spatial reasoning, and to be robust for semantic and systematic biases.
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MARBLE: A Hard Benchmark for Multimodal Spatial Reasoning and Planning
State-of-the-art multimodal language models perform at or near random chance on MARBLE, a new hard benchmark for spatial reasoning and planning.
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