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

• URL: http://arxiv.org/abs/2502.03766v1
• Date: Thu, 06 Feb 2025 04:01:27 GMT
• Title: Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models
• Authors: Meiquan Dong, Haoran Liu, Yan Huang, Zixuan Feng, Jianhong Tang, Ruoxi Wang,
• Abstract summary: The organization of latent token representations plays a crucial role in determining the stability, generalization, and contextual consistency of language models. A hierarchical alignment method was introduced to token embeddings without altering core model weights. Experimental evaluations demonstrated improvements in rare token retrieval, adversarial, and long-range dependency tracking.
• Score: 7.798982346197703
• License:
• 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.

Related papers

• Statistical Coherence Alignment for Large Language Model Representation Learning Through Tensor Field Convergence [0.0]
  Representation learning plays a central role in structuring internal embeddings to capture statistical properties of language. Coherence alignment is introduced as a method to enforce structured token representations through tensor field convergence. Empirical evaluations demonstrate that applying coherence constraints improves perplexity, enhances classification accuracy, and refines rare word embeddings.
  arXiv  Detail & Related papers  (2025-02-13T23:24:25Z)
• Structured Convergence in Large Language Model Representations via Hierarchical Latent Space Folding [0.0]
  Token representations in high-dimensional latent spaces often exhibit redundancy, limiting computational efficiency and reducing structural coherence across model layers. This paper introduces a structured transformation mechanism that enforces a multi-scale organization within learned embeddings. Empirical evaluation demonstrates a reduction in representational variance across layers, contributing to more stable perplexity distributions and enhancing predictive confidence in text generation.
  arXiv  Detail & Related papers  (2025-02-13T04:01:54Z)
• Lexical Manifold Reconfiguration in Large Language Models: A Novel Architectural Approach for Contextual Modulation [0.0]
  A structured approach was developed for dynamically reconfiguring token embeddings through continuous geometric transformations. A manifold-based transformation mechanism was integrated to regulate lexical positioning, allowing embeddings to undergo controlled shifts. Empirical evaluations demonstrated that embedding reconfiguration contributed to reductions in perplexity, improved lexical coherence, and enhanced sentence-level continuity.
  arXiv  Detail & Related papers  (2025-02-12T22:11:07Z)
• Contextual Subspace Manifold Projection for Structural Refinement of Large Language Model Representations [0.0]
  Internal representations within deep neural architectures encode high-dimensional abstractions of linguistic structures. This paper introduces a structured refinement technique that selectively reconfigures token embeddings through controlled subspace constraints. Empirical evaluations demonstrated that the structured intervention reduced anisotropy, leading to improved representation compactness.
  arXiv  Detail & Related papers  (2025-02-12T00:00:37Z)
• Hierarchical Lexical Manifold Projection in Large Language Models: A Novel Mechanism for Multi-Scale Semantic Representation [0.0]
  The integration of structured hierarchical embeddings into transformer-based architectures introduces a refined approach to lexical representation. A projection mechanism that maps tokens onto a structured manifold provides improved lexical alignment. The refined hierarchical organization of embeddings provides greater interpretability in lexical modeling.
  arXiv  Detail & Related papers  (2025-02-08T00:49:32Z)
• Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations [75.14793516745374]
  We propose to strengthen the structural inductive bias of a Transformer by intermediate pre-training. Our experiments confirm that this helps with few-shot learning of syntactic tasks such as chunking. Our analysis shows that the intermediate pre-training leads to attention heads that keep track of which syntactic transformation needs to be applied to which token.
  arXiv  Detail & Related papers  (2024-07-05T14:29:44Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Autoregressive Structured Prediction with Language Models [73.11519625765301]
  We describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs. Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at.
  arXiv  Detail & Related papers  (2022-10-26T13:27:26Z)
• Model Criticism for Long-Form Text Generation [113.13900836015122]
  We apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of generated text. We perform experiments on three representative aspects of high-level discourse -- coherence, coreference, and topicality. We find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference.
  arXiv  Detail & Related papers  (2022-10-16T04:35:58Z)
• Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation [54.864148836486166]
  We propose to incorporate the explicit syntactic and semantic structures of languages into a non-autoregressive Transformer. Our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several state-of-the-art non-autoregressive models.
  arXiv  Detail & Related papers  (2021-01-22T04:12:17Z)
• Target-Embedding Autoencoders for Supervised Representation Learning [111.07204912245841]
  This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional. We motivate and formalize the general framework of target-embedding autoencoders (TEA) for supervised prediction, learning intermediate latent representations jointly optimized to be both predictable from features as well as predictive of targets.
  arXiv  Detail & Related papers  (2020-01-23T02:37:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.