Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models

TL;DR

This paper proposes a hierarchical alignment method to restructure token embeddings in large language models without modifying core weights, improving stability, robustness, and interpretability of latent representations.

Contribution

It introduces a novel hierarchical alignment technique that refines latent token representations without additional computational overhead, enhancing model robustness and interpretability.

Findings

Improved rare token retrieval and adversarial robustness.

Enhanced long-range dependency tracking.

Maintained computational efficiency with representational gains.

Abstract

The organization of latent token representations plays a crucial role in determining the stability, generalization, and contextual consistency of language models, yet conventional approaches to embedding refinement often rely on parameter modifications that introduce additional computational overhead. A hierarchical alignment method was introduced to restructure token embeddings without altering core model weights, ensuring that representational distributions maintained coherence across different linguistic contexts. Experimental evaluations demonstrated improvements in rare token retrieval, adversarial robustness, and long-range dependency tracking, highlighting the advantages of hierarchical structuring in mitigating inconsistencies in latent space organization. The comparative analysis against conventional fine-tuning and embedding perturbation methods revealed that hierarchical restructuring maintained computational efficiency while achieving measurable gains in representation quality. Structural refinements introduced through the alignment process resulted in improved contextual stability across varied linguistic tasks, reducing inconsistencies in token proximity relationships and enhancing interpretability in language generation. A detailed computational assessment confirmed that the realignment process introduced minimal inference overhead, ensuring that representational improvements did not compromise model efficiency. The findings reinforced the broader significance of structured representation learning, illustrating that hierarchical embedding modifications could serve as an effective strategy for refining latent space distributions while preserving pre-learned semantic associations.