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Research Frontiers in Neural Language Models and Syntactic Representation

Investigates how deep neural networks learn and encode grammatical structures in their hidden layers during language processing.

Emergent Grammatical Structure in Transformer Hidden States
Syntactic Abstraction Without Explicit Parse Trees
Cross-Linguistic Universals Encoded in Neural Embeddings
Compositional Meaning Assembly in Attention Mechanisms
Latent Hierarchies and Recursive Depth in Language Models
Dependency Relations Without Supervised Syntactic Annotation
Probing the Geometry of Linguistic Constraints in Embeddings
Agreement and Long-Range Dependencies in Neural Representations
Typological Variation Reflected in Model Architecture Choices
Adversarial Robustness of Syntactic Knowledge in Transformers

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