3 papers
cs.CL2026
The Frequency Confound in Language-Model Surprisal and Metaphor Novelty
Omar Momen, Sina ZarrieÃ
Language-model (LM) surprisal is widely used as a proxy for contextual predictability and has been reported to correlate with metaphor novelty judgments. However, surprisal is tigh…
cs.CL2026
Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets
Omar Momen, Emilie Sitter, Berenike Herrmann +1
Novel metaphor comprehension involves complex semantic processes and linguistic creativity, making it an interesting task for studying language models (LMs). This study investigate…
cs.CL2025
Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)
Francesca Padovani, Bastian Bunzeck, Manar Ali +4
We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we empl…