Generalizations across filler-gap dependencies in neural language models

Katherine Howitt; Sathvik Nair; Allison Dods; Robert Melvin Hopkins

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arXiv · 2410.18225

Generalizations across filler-gap dependencies in neural language models

Katherine Howitt·Sathvik Nair·Allison Dods·Robert Melvin Hopkins

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Abstract

Humans develop their grammars by making structural generalizations from finite input. We ask how filler-gap dependencies, which share a structural generalization despite diverse surface forms, might arise from the input. We explicitly control the input to a neural language model (NLM) to uncover whether the model posits a shared representation for filler-gap dependencies. We show that while NLMs do have success differentiating grammatical from ungrammatical filler-gap dependencies, they rely on superficial properties of the input, rather than on a shared generalization. Our work highlights the need for specific linguistic inductive biases to model language acquisition.

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Katherine Howitt, Sathvik Nair, Allison Dods, Robert Melvin Hopkins. 2024-10-23. Generalizations across filler-gap dependencies in neural language models. https://arxiv.org/abs/2410.18225

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