Contextual Morphogenesis in Large Language Models: A Novel Approach to Self-Organizing Token Representations

• URL: http://arxiv.org/abs/2502.00301v1
• Date: Sat, 01 Feb 2025 03:50:46 GMT
• Title: Contextual Morphogenesis in Large Language Models: A Novel Approach to Self-Organizing Token Representations
• Authors: Alistair Dombrowski, Beatrix Engelhardt, Dimitri Fairbrother, Henry Evidail,
• Abstract summary: contextual morphogenesis establishes a self-organizing mechanism that restructures token boundaries based on learned contextual dependencies. Empirical evaluations demonstrate that dynamically adjusted tokenization contributes to reductions in perplexity while maintaining representational stability. Comparative assessments across different linguistic corpora suggest that adaptive tokenization preserves interpretability while improving alignment with contextual cues. The effectiveness of contextual morphogenesis in refining structural stability and predictive performance highlights its viability as an alternative to traditional tokenization methods.
• Score: 0.0
• License:
• Abstract: Token representations influence the efficiency and adaptability of language models, yet conventional tokenization strategies impose rigid segmentation boundaries that do not adjust dynamically to evolving contextual relationships. The introduction of contextual morphogenesis establishes a self-organizing mechanism that restructures token boundaries based on learned contextual dependencies, allowing embeddings to evolve progressively across iterative processing steps. Empirical evaluations demonstrate that dynamically adjusted tokenization contributes to reductions in perplexity while maintaining representational stability, particularly in linguistically complex domains where static segmentation fails to capture nuanced dependencies. Computational trade-offs associated with self-organizing token structures indicate that additional processing overhead remains within feasible limits, provided that optimization strategies account for segmentation update efficiency. Comparative assessments across different linguistic corpora suggest that adaptive tokenization preserves interpretability while improving alignment with contextual cues, reinforcing the potential of morphogenetic segmentation mechanisms to refine predictive accuracy. Stability analyses confirm that evolving token structures maintain consistent segmentation behaviors across varied text distributions, ensuring that representational adaptations remain linguistically coherent. The effectiveness of contextual morphogenesis in refining structural stability and predictive performance highlights its viability as an alternative to traditional tokenization methods. Further analysis of computational efficiency considerations suggests that hybrid strategies integrating both static and dynamic segmentation techniques may offer a balanced approach to optimizing representational flexibility while maintaining inference efficiency.

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)
• 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)
• 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 Structure Modulation in Large Language Models Through Stochastic Concept Embedding Transitions [0.0]
  embedding transitions introduce a probabilistic mechanism for adjusting token representations dynamically during inference. A transition framework was proposed in which each token embedding evolved through probabilistic updates. Empirical evaluations demonstrated greater lexical diversity, improved generative coherence, and enhanced retention of low-frequency vocabulary.
  arXiv  Detail & Related papers  (2025-02-08T12:53:52Z)
• 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)
• 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)
• Flow Factorized Representation Learning [109.51947536586677]
  We introduce a generative model which specifies a distinct set of latent probability paths that define different input transformations. We show that our model achieves higher likelihoods on standard representation learning benchmarks while simultaneously being closer to approximately equivariant models.
  arXiv  Detail & Related papers  (2023-09-22T20:15:37Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)
• Self-supervised Augmentation Consistency for Adapting Semantic Segmentation [56.91850268635183]
  We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. We employ standard data augmentation techniques $-$ photometric noise, flipping and scaling $-$ and ensure consistency of the semantic predictions. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios.
  arXiv  Detail & Related papers  (2021-04-30T21:32:40Z)

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.