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Deep neural networks and humans both benefit from compositional language structure
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Deep neural networks and humans both benefit from compositional language structure
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Deep neural networks and humans both benefit from compositional language structure
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Journal Article
Deep neural networks and humans both benefit from compositional language structure
2024
Overview
Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional and transparent structures are typically easier to learn than those with opaque and irregular structures. However, this learnability advantage has not yet been shown for deep neural networks, limiting their use as models for human language learning. Here, we directly test how neural networks compare to humans in learning and generalizing different languages that vary in their degree of compositional structure. We evaluate the memorization and generalization capabilities of a large language model and recurrent neural networks, and show that both deep neural networks exhibit a learnability advantage for more structured linguistic input: neural networks exposed to more compositional languages show more systematic generalization, greater agreement between different agents, and greater similarity to human learners.
This study demonstrates that deep neural networks, like humans, show a learnability advantage when trained on languages with more structured linguistic input, resulting in closer alignment with human learning. This finding has important implications for both understanding human language acquisition and designing artificial language systems.
