GiLT Enhances Transformers with Dependency Graphs

Researchers have introduced GiLT, a novel approach to augmenting Transformer language models using dependency graphs, according to a preprint published on arXiv. The Graph-Infused Layers Transformer Language Model improves syntactic generalization without requiring structural tokens, maintaining perplexity performance while diverging from prior methods that relied on constituency tree structures.

Traditional approaches to enhancing Transformers with linguistic structures have focused on syntactic tree frameworks, particularly constituency trees. GiLT, however, leverages dependency graphs to integrate syntactic information directly into model layers. This method avoids inserting structural tokens into input sequences, a common practice in previous techniques that added complexity to model training and inference.

The authors report that GiLT achieves competitive results on standard language modeling benchmarks while demonstrating stronger syntactic generalization capabilities. By encoding dependency relationships between words as graph structures, the model retains contextual information while better capturing grammatical patterns across diverse linguistic inputs.

Transformers, the foundational architecture for modern language models, often struggle with syntactic generalization—the ability to apply grammatical rules to novel sentence structures. This research offers a computationally efficient solution by embedding graph-based syntactic representations directly into attention mechanisms, potentially reducing the need for explicit token-level structural annotations.