Many of the thousands of attested languages share common configurations of features, creating a spectrum from typologically very rare (e.g., object-verb-subject word order) or impossible languages to very common combinations of features (e.g., subject-object-verb word order). One central question is under what conditions such typological tendencies can be predicted, and specifically whether the learning bias of language models (LMs) is sufficient to reproduce such patterns. In this study, we add one dimensionality to such analysis -- the learning scenario for LMs -- to explore its interaction with the inductive bias of LMs. Specifically, as a first study, we examine the effect of curriculum learning (CL), as a developmentally motivated learning scenario, i.e., starting with simpler sentences rather than randomly-ordered input. We expand existing LM-based exploration (El-Naggar et al., 2025a,b) with a simple CL variant and find that CL substantially impacts the apparent inductive bias of LMs.

翻译：众多有记载的语言共享特征配置，形成了从类型学上极为罕见（如动宾主语语序）或不可能存在的语言到非常常见的特征组合（如主宾动语序）的连续谱系。核心问题在于：何种条件下可预测此类类型学倾向，以及语言模型的学习偏差是否足以复现这些模式。本研究通过引入学习场景这一新维度，探索其与语言模型归纳偏好的交互作用。具体而言，作为首次系统性探索，我们考察了课程学习（一种发展性学习场景，即从简单句子而非随机顺序输入开始训练）的影响。基于现有语言模型研究 (El-Naggar et al., 2025a,b)，我们通过简单课程学习变体进行扩展，发现课程学习显著改变了语言模型的表观归纳偏好。