3 papers
cs.CL2026
Entity tracking emerges in sub-billion parameter language models and exceeds human performance in naturalistic narratives
Karolina Drożdż, Micha Heilbron
Understanding language requires tracking entities across discourse - i.e., knowing where things are and how they change, even when not explicitly stated. Whether language models pe…
cs.CL2025
Human-like fleeting memory improves language learning but impairs reading time prediction in transformer language models
Abishek Thamma, Micha Heilbron
Human memory is fleeting. As words are processed, the exact wordforms that make up incoming sentences are rapidly lost. Cognitive scientists have long believed that this limitation…
q-bio.NC2019
Tracking Naturalistic Linguistic Predictions with Deep Neural Language Models
Micha Heilbron, Benedikt Ehinger, Peter Hagoort +1
Prediction in language has traditionally been studied using simple designs in which neural responses to expected and unexpected words are compared in a categorical fashion. However…