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Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task
Bart Evelo, Meaghan Fowlie, Denis Paperno
Do neural models, such as Large Language Models, genuinely acquire compositional abilities for interpretation of natural language? When we talk about semantic interpretation, we ca…
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…
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…
Context-aware Visual Storytelling with Visual Prefix Tuning and Contrastive Learning
Yingjin Song, Denis Paperno, Albert Gatt
Visual storytelling systems generate multi-sentence stories from image sequences. In this task, capturing contextual information and bridging visual variation bring additional chal…
The Scenario Refiner: Grounding subjects in images at the morphological level
Claudia Tagliaferri, Sofia Axioti, Albert Gatt +1
Derivationally related words, such as "runner" and "running", exhibit semantic differences which also elicit different visual scenarios. In this paper, we ask whether Vision and La…