Neural network language models (LMs) have been shown to successfully capture complex linguistic knowledge. However, their utility for understanding language acquisition is still debated. We contribute to this debate by presenting a case study where we use LMs as simulated learners to derive novel experimental hypotheses to be tested with humans. We apply this paradigm to study cross-dative generalization (CDG): productive generalization of novel verbs across dative constructions (she pilked me the ball/she pilked the ball to me) -- acquisition of which is known to involve a large space of contextual features -- using LMs trained on child-directed speech. We specifically ask: "what properties of the training exposure facilitate a novel verb's generalization to the (unmodeled) alternate construction?" To answer this, we systematically vary the exposure context in which a novel dative verb occurs in terms of the properties of the theme and recipient, and then analyze the LMs' usage of the novel verb in the unmodeled dative construction. We find LMs to replicate known patterns of children's CDG, as a precondition to exploring novel hypotheses. Subsequent simulations reveal a nuanced role of the features of the novel verbs' exposure context on the LMs' CDG. We find CDG to be facilitated when the first postverbal argument of the exposure context is pronominal, definite, short, and conforms to the prototypical animacy expectations of the exposure dative. These patterns are characteristic of harmonic alignment in datives, where the argument with features ranking higher on the discourse prominence scale tends to precede the other. This gives rise to a novel hypothesis that CDG is facilitated insofar as the features of the exposure context -- in particular, its first postverbal argument -- are harmonically aligned. We conclude by proposing future experiments that can test this hypothesis in children.

翻译：神经网络语言模型已被证明能够成功捕捉复杂的语言学知识。然而，其在理解语言习得方面的效用仍存在争议。我们通过一项案例研究参与此辩论：使用语言模型作为模拟学习者，推导出可供人类测试的新颖实验假设。我们将此范式应用于研究跨与格泛化——即新颖动词在双宾结构与介词与格结构之间的能产性泛化（例如"她给我皮尔克了球"/"她把球皮尔克给了我"），其习得过程已知涉及大量语境特征空间。本研究使用儿童导向语料训练的语言模型，具体探究："训练暴露的哪些特性能够促进新颖动词向（未建模的）交替结构泛化？" 为解答此问题，我们系统性地改变新颖与格动词在暴露语境中的出现方式，调整其客体与接受者的属性特征，进而分析语言模型在未建模与格结构中使用该新颖动词的模式。研究发现语言模型能够复现儿童跨与格泛化的已知模式，这为探索新假设提供了前提条件。后续模拟揭示了暴露语境特征对语言模型跨与格泛化产生的微妙影响：当暴露语境中动词后首个论元具有代词性、定指性、短小性且符合典型与格结构的生命度预期时，跨与格泛化更易发生。这些模式体现了与格结构中和谐对齐的特性——在话语突显层级上排名更高的论元特征倾向于前置。由此催生了一个新颖假设：当暴露语境（特别是其动词后首个论元）的特征呈现和谐对齐时，跨与格泛化将得到促进。最后我们提出了可在儿童群体中验证该假设的未来实验方案。