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20232026
most citedBurn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences?

1 citations · 1 across the 6 of their papers we have counts for

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Showing 2025 · cs.CLShow all

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cs.CL2025

Don't Learn, Ground: A Case for Natural Language Inference with Visual Grounding

Daniil Ignatev, Ayman Santeer, Albert Gatt +1

We propose a zero-shot method for Natural Language Inference (NLI) that leverages multimodal representations by grounding language in visual contexts. Our approach generates visual…

cs.CL2025★ 1 cited

Burn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences?

Yingjin Song, Yupei Du, Denis Paperno +1

This paper introduces the TempVS benchmark, which focuses on temporal grounding and reasoning capabilities of Multimodal Large Language Models (MLLMs) in image sequences. TempVS co…