Gradient-Regularized Latent Space Modulation in Large Language Models for Structured Contextual Synthesis

• URL: http://arxiv.org/abs/2502.01979v1
• Date: Tue, 04 Feb 2025 03:43:52 GMT
• Title: Gradient-Regularized Latent Space Modulation in Large Language Models for Structured Contextual Synthesis
• Authors: Derek Yotheringhay, Beatrix Nightingale, Maximilian Featherstone, Edmund Worthington, Hugo Ashdown,
• Abstract summary: This paper introduces a novel paradigm for guiding text generation through the application of structured constraints within the latent space. The integration of gradient-based regularizations mitigates abrupt variations in latent representations. The framework substantially reduces structural inconsistencies while preserving the generative flexibility inherent in neural models.
• Score: 0.0
• License:
• Abstract: Generating structured textual content requires mechanisms that enforce coherence, stability, and adherence to predefined constraints while maintaining semantic fidelity. Conventional approaches often rely on rule-based heuristics or fine-tuning strategies that lack flexibility and generalizability across diverse tasks. The incorporation of Gradient-Regularized Latent Space Modulation (GRLSM) introduces a novel paradigm for guiding text generation through the application of structured constraints within the latent space. The integration of gradient-based regularization mitigates abrupt variations in latent representations, ensuring a smoother encoding process that enhances structural consistency and logical progression within generated sequences. Comparative evaluations demonstrate that latent space modulation leads to a reduction in perplexity, increased coherence scores, and improved structural alignment across multiple domains. Stability assessments further indicate that the imposition of spectral norm constraints facilitates more controlled variations in generated text, preserving semantic consistency under input perturbations. Empirical results confirm that structured latent space constraints not only refine the organization of generated outputs but also enhance interpretability through more predictable and reliable synthesis patterns. Performance metrics illustrate that the GRLSM framework substantially reduces structural inconsistencies while preserving the generative flexibility inherent in neural models.

Related papers

• Exploring Contextual Flux in Large Language Models: A Novel Approach to Self-Modulating Semantic Networks [0.0]
  Self-modulating mechanisms introduce dynamic adaptation capabilities within language models. contextual realignment strategies influence token embedding trajectories across extended sequences. Self-regulation enhances text generation consistency while preserving generative flexibility. Findings suggest that while adaptive embedding updates improve certain aspects of coherence, their impact remains contingent on model capacity and input complexity.
  arXiv  Detail & Related papers  (2025-02-16T01:08:19Z)
• 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)
• Latent Convergence Modulation in Large Language Models: A Novel Approach to Iterative Contextual Realignment [0.0]
  A structured modulation mechanism was introduced to regulate hidden state transitions. Lattice adjustments contributed to reductions in perplexity fluctuations, entropy variance, and lexical instability.
  arXiv  Detail & Related papers  (2025-02-10T09:46:33Z)
• Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models [7.798982346197703]
  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.
  arXiv  Detail & Related papers  (2025-02-06T04:01:27Z)
• Context-Preserving Gradient Modulation for Large Language Models: A Novel Approach to Semantic Consistency in Long-Form Text Generation [0.19791587637442667]
  A novel modulation gradient approach is introduced to adjust parameter updates dynamically in response to contextual relevance. The proposed method enhances the stability of model-generated narratives without imposing significant computational overhead.
  arXiv  Detail & Related papers  (2025-02-05T22:13:06Z)
• Sequential Representation Learning via Static-Dynamic Conditional Disentanglement [58.19137637859017]
  This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables. Experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.
  arXiv  Detail & Related papers  (2024-08-10T17:04:39Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Illumination and Reflection Decomposition [68.6707284662443]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic and static scenes plagued by severe invisibility and noise. One critical aspect is formulating a consistency constraint specifically for temporal-spatial illumination and appearance enhanced versions. We present an innovative video Retinex-based decomposition strategy that operates without the need for explicit supervision.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• Sequential Visual and Semantic Consistency for Semi-supervised Text Recognition [56.968108142307976]
  Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. Most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performance of STR models. This paper proposes a novel semi-supervised learning method for STR that incorporates word-level consistency regularization from both visual and semantic aspects.
  arXiv  Detail & Related papers  (2024-02-24T13:00:54Z)
• 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)
• Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application [49.66088514485446]
  Best-Response Constraint (BRC) is a general learning framework to explicitly formulate the potential dependency of the generator on the discriminator. We show that even with different motivations and formulations, a variety of existing GANs ALL can be uniformly improved by our flexible BRC methodology.
  arXiv  Detail & Related papers  (2022-05-20T12:42:41Z)
• 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.