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This paper investigates how large language models handle the combination of negation and figurative language, finding that this combination poses a particular challenge and that performance depends heavily on prompt style. The authors develop new annotations for the Fig-QA dataset and analyze embedding spaces to uncover additional linguistic factors like tense and concreteness.
MIT researchers release the first multilingual negation benchmark covering seven languages and show VLMs like CLIP struggle with non-Latin scripts, while MultiCLIP and SpaceVLM offer uneven improvements across languages.