13 papers
Relational Attention for Data-Efficient Language Modeling
Adrian Brasoveanu, Ece Takmaz, Jakub Dotlačil
We present Relational BabyLM, a system submission to the BabyLM 2026 challenge that combines two cognitively motivated inductive biases in a single decoder-only Transformer. Archit…
When Context Misleads: Surprisal, Energy and Attention Entropy as Metrics of Coherence Illusions in LLMs
Ece Takmaz, Nitin Kumar, Li Kloostra +1
Psycholinguistics studies show that human readers fall for coherence illusions: an incoherent discourse can seem coherent simply because a distractor matches what comes next. We in…
Energy-Based Transformers as Predictors of Reading Difficulty
Jakub Dotlacil, Ece Takmaz
Transformer language models have become established tools for modeling human sentence processing, with measures such as surprisal and attention entropy serving as effective predict…
Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models
Ece Takmaz, Lisa Bylinina, Jakub Dotlacil
State-of-the-art vision-and-language models consist of many parameters and learn from enormous datasets, surpassing the amounts of linguistic data that children are exposed to as t…
Correlates of Image Memorability in Vision Encoders: Activations, Attention Entropy, Patch Uniformity and Autoencoder Losses
Ece Takmaz, Albert Gatt, Jakub Dotlacil
Images vary in how memorable they are to humans. Inspired by findings from cognitive science and computer vision, we explore correlates of image memorability in pretrained transfor…
Common Objects Out of Context (COOCo): Investigating Multimodal Context and Semantic Scene Violations in Referential Communication
Filippo Merlo, Ece Takmaz, Wenkai Chen +1
To what degree and under what conditions do VLMs rely on scene context when generating references to objects? To address this question, we introduce the $\textit{Common Objects Out…