Abstract
How can children acquire native-level syntax from limited input? According to the Poverty of the Stimulus Hypothesis (PoSH), the linguistic input children receive is insufficient to explain certain generalizations that are robustly learned; innate linguistic constraints, many have argued, are thus necessary to explain language learning. Neural language models, which lack such language-specific constraints in their design, offer a computational test of this longstanding (but controversial) claim. We introduce \poshbench, a training-and-evaluation suite targeting question formation, islands to movement, and other English phenomena at the center of the PoSH arguments. Training Transformer models on 10--50M words of developmentally plausible text, we find indications of generalization on all phenomena even without direct positive evidence -- yet neural models remain less data-efficient and their generalizations are weaker than those of children. We further enhance our models with three recently proposed cognitively motivated inductive biases. We find these biases improve general syntactic competence but not \poshbench performance. Our findings challenge the claim that innate syntax is the only possible route to generalization, while suggesting that human-like data efficiency requires inductive biases beyond those tested here.
Abstract (translated)
孩子们如何从有限的语言输入中掌握母语级别的语法结构？根据“语言刺激不足假设”（Poverty of the Stimulus Hypothesis，简称 PoSH），孩子们接收到的言语输入不足以解释某些被牢固学习的一般化现象；因此，许多研究者认为先天的语言限制是必要的。神经语言模型的设计中缺乏这种特定于语言的约束条件，为这一长久存在但颇具争议的论点提供了一个计算测试手段。 我们介绍了一套名为 \poshbench 的训练和评估工具集，该工具针对提问结构、运动中的岛现象及其他处于 PoSH 论据中心的英语现象。在使用10至50百万个开发阶段可信文本进行训练后，即使没有直接的正面证据，我们也发现了所有现象上的泛化迹象——但神经模型仍不如孩子那样数据高效且泛化效果较弱。 进一步地，我们通过三种最近提出的认知动机归纳偏置来改进我们的模型。发现这些偏置提高了模型的一般句法能力，但却未能提升 \poshbench 表现。研究结果挑战了先天语法是通向泛化的唯一途径这一论断，并暗示人类级别的数据效率需要超出此次测试范围的其他归纳偏置。
URL
https://arxiv.org/abs/2602.09992
