Papers with TreeReg

1 papers
Sneaking Syntax into Transformer Language Models with Tree Regularization (2025.naacl-long)

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Challenge: Existing methods for incorporating syntactic inductive biases into transformers are limited . we introduce auxiliary loss function that converts bracketing decisions into differentiable orthogonality constraints on vector hidden states.
Approach: They propose to introduce syntactic inductive biases into transformer circuits through a structured regularizer.
Outcome: The proposed approach could unlock more robust and data-efficient learning in transformer language models . it integrates seamlessly with the standard LM objective, requiring no architectural changes.

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