This article discusses the V-FLUTE dataset, aimed at improving AI's grasp of figurative language within visual contexts. It includes 6,027 examples featuring metaphors, idioms, similes, sarcasm, and humor, serving as a tool for benchmarking vision-language models' performance. The analysis involves both automatic evaluations and human assessments, aiming to highlight significant shortcomings in current models and to drive further research into improving AI's interpretative abilities in multimodal settings, specifically in understanding complex figurative expressions.
Our dataset, V-FLUTE, comprises 6,027 instances covering various figurative languages such as metaphor, simile, sarcasm, and humor, essential for evaluating VLMs.
Through benchmarking state-of-the-art vision-language models with V-FLUTE, we pinpoint critical areas for improvement, emphasizing the importance of understanding figurative language in AI.
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