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20242026
most citedExperiential Semantic Information and Brain Alignment: Are Multimodal Models Better than Language Models?

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2026

What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?

Anna Bavaresco, Ina Klarić, Raquel Fernández +1

Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivi…

cs.CL20251 cited

Experiential Semantic Information and Brain Alignment: Are Multimodal Models Better than Language Models?

Anna Bavaresco, Raquel Fernández

A common assumption in Computational Linguistics is that text representations learnt by multimodal models are richer and more human-like than those by language-only models, as they…

cs.CL2024

Vision-Language Models Align with Human Neural Representations in Concept Processing

Anna Bavaresco, Marianne de Heer Kloots, Sandro Pezzelle +1

Recent studies suggest that transformer-based vision-language models (VLMs) capture the multimodality of concept processing in the human brain. However, a systematic evaluation exp…

cs.CL2024

LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi +17

There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reprodu…

cs.CL2024

Don't Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models

A. Bavaresco, A. Testoni, R. Fernández

Image-based advertisements are complex multimodal stimuli that often contain unusual visual elements and figurative language. Previous research on automatic ad understanding has re…