ArticleNature communications2024
Deep neural networks and humans both benefit from compositional language structure.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Compositionality and systematicity emerge from iterated learning in deep linear networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Soft Sensor Modeling Method Based on Local Migration Modeling Framework.Sensors (Basel, Switzerland) · 2025Article
- Effectiveness of the GPT-4o Model in Interpreting Electrocardiogram Images for Cardiac Diagnostics: Diagnostic Accuracy Study.JMIR AI · 2025Article
- Deep neural networks and humans both benefit from compositional language structure.Nature communications · 2024Article
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3 authors.
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Abstract
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.
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