This article continues a study on how large AI models deal with figurative language, emphasizing the development of the V-FLUTE dataset. FLUTE is used to train models on explainable textual entailment. Drawing from past research, this study seeks to expand understanding of figurative language to multimodal contexts, testing AI's grasp of nonliteral meanings in both images and text. The advancements spotlight a shift towards creating AI models that not only predict labels but also provide insightful explanations, enhancing their interpretability.
Large AI models are improving in handling figurative language through datasets like FLUTE and V-FLUTE, which emphasize explainability in textual and multimodal settings.
The V-FLUTE dataset extends figurative language understanding tasks into the visual modality, allowing models to assess nonliteral meanings in both images and text.
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