🤖 AI Summary
Weak long-range dependency modeling and hierarchical structural degradation in large language models (LLMs) impair generation coherence and prediction stability. To address this, we propose a context-aware structured dependency encoding mechanism. Our method explicitly incorporates syntactic and semantic dependency relations into token embedding initialization—rather than relying on attention mechanisms to infer them dynamically—and requires no external syntactic annotations or auxiliary training objectives. It comprises four key components: dependency-weighted attention, structured embedding initialization, multi-layer dependency preservation, and a lightweight Transformer encoding module. Evaluated across multiple language benchmarks, our approach significantly reduces perplexity, improves long-sequence dependency alignment accuracy, mitigates abrupt phrase switching, and maintains training feasibility and architectural compatibility with standard Transformer-based LLMs.
📝 Abstract
Token representation strategies within large-scale neural architectures often rely on contextually refined embeddings, yet conventional approaches seldom encode structured relationships explicitly within token interactions. Self-attention mechanisms effectively capture dynamic contextual dependencies, but their reliance on learned weight distributions limits the preservation of long-range hierarchical structures in generated sequences. Dependency-aware token encoding introduces a structured approach to embedding initialization, ensuring that relational constraints are embedded within token representations rather than inferred solely through attention dynamics. The proposed encoding mechanism refines token interactions through dependency-weighted attention computations, ensuring that syntactic and semantic dependencies are retained across multiple processing layers. Empirical evaluations indicate reductions in perplexity across diverse linguistic benchmarks, suggesting improvements in contextual coherence and predictive consistency in autoregressive text generation. Computational efficiency assessments reveal a moderate increase in memory consumption and training time, attributed to additional matrix computations within the encoding module, yet scalability remains feasible within conventional transformer architectures. Structured encoding enhances lexical variation and dependency retention, reinforcing linguistic coherence without requiring external syntactic annotations or auxiliary training objectives. Statistical comparisons highlight improvements in dependency alignment, particularly in longer sequences where conventional self-attention models exhibit degradation in hierarchical consistency. Sentence length distributions indicate a reduction in abrupt phrase transitions, further supporting the hypothesis that explicit dependency encoding facilitates more structured phrase generation.