Neural Language Models Learn the Contextual Distributions of Dependency Structures: a statistical learning theory to compositionality

arXiv:2609.34936 · cs.CL · Submitted 2026-09-28 · Read on arXiv

cs.CL

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Transformer — 14× in this paper · explained in 2 episodes
• Core — 12× in this paper · explained in 2 episodes
• Generalization — 10× in this paper · explained in 13 episodes
• Clustering — 9× in this paper · explained in 2 episodes
• Alternation — 4× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Masked Language Modeling — 1× in this paper
• Pragmatics — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes

Sources

• Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers
• Developmental approach reveals the statistical learning of Neural Language Models: Transformers generalize from the most abstract statistical patterns
• Transformers perform adaptive partial pooling

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