Tootfinder

Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages
Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe
https://arxiv.org/abs/2510.12722

Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages
Whether language models (LMs) have inductive biases that favor typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using artificial languages (ALs) (White and Cotterell, 2021; Kuribayashi et al., 2024). In this paper, we extend these works from two perspectives. First, we extend their context-free AL formalization by adopting Generalized Categorial Grammar (GCG) (Wood, 2014), which allows ALs to cover attested but previously overlooked cons…