  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.
