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cs.CL2026
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…
cs.CL2026
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…
cs.CL2025
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…