Architectural Fusion Through Contextual Partitioning in Large Language Models: A Novel Approach to Parameterized Knowledge Integration

• URL: http://arxiv.org/abs/2501.12901v1
• Date: Wed, 22 Jan 2025 14:21:04 GMT
• Title: Architectural Fusion Through Contextual Partitioning in Large Language Models: A Novel Approach to Parameterized Knowledge Integration
• Authors: Offa Kingsleigh, Alfred Abercrombie, David Woolstencroft, Beorhtric Meadowcroft, Marcus Irvin,
• Abstract summary: This paper introduces an innovative approach to enhancing the architectural design of large-scale computational models through the dynamic segmentation of parameters into context-aware regions. Experimental evaluations demonstrate substantial improvements in accuracy, perplexity, and contextual coherence across a variety of linguistic tasks. The findings collectively demonstrate the potential for Contextual Partitioning to redefine the scalability and adaptability of computational language architectures in diverse and complex domains.
• Score: 0.0
• License:
• Abstract: Contextual Partitioning introduces an innovative approach to enhancing the architectural design of large-scale computational models through the dynamic segmentation of parameters into context-aware regions. This methodology emphasizes the importance of task-specific specialization, achieved through adaptive parameter allocation mechanisms that align with the linguistic features of input data. Experimental evaluations demonstrated substantial improvements in accuracy, perplexity, and contextual coherence across a variety of linguistic tasks, highlighting the adaptability and scalability of the proposed framework. By reducing redundancy and enhancing computational efficiency, Contextual Partitioning not only streamlines model operations but also expands the scope of applications for advanced language processing systems. The approach operates autonomously, requiring no external fine-tuning, thereby addressing a significant limitation in conventional parameter optimization techniques. Empirical results demonstrate the effectiveness of gradient-driven segmentation, enabling models to dynamically recalibrate and specialize in response to task-specific demands. Furthermore, resource utilization metrics reveal notable reductions in memory usage and training times, confirming the efficiency of the approach. Observations from qualitative analyses illustrate improved contextual coherence and logical flow in generated outputs, reinforcing the practical value of this technique. The findings collectively demonstrate the potential for Contextual Partitioning to redefine the scalability and adaptability of computational language architectures in diverse and complex domains.

Related papers

• Learning Task Representations from In-Context Learning [73.72066284711462]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning. We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads. We show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• Structural Embedding Projection for Contextual Large Language Model Inference [0.0]
  Structured embedding transformations offer a promising approach for enhancing the efficiency and coherence of language model inference. The mathematical formulation of Structural Embedding Projection (SEP) enables embedding spaces to capture structured contextual relationships. The impact of SEP on lexical diversity suggested that embedding modifications influenced the model's vocabulary usage.
  arXiv  Detail & Related papers  (2025-01-31T00:46:21Z)
• Contextual Reinforcement in Multimodal Token Compression for Large Language Models [0.0]
  token compression remains a critical challenge for scaling models to handle increasingly complex and diverse datasets. A novel mechanism based on contextual reinforcement is introduced, dynamically adjusting token importance through interdependencies and semantic relevance. This approach enables substantial reductions in token usage while preserving the quality and coherence of information representation.
  arXiv  Detail & Related papers  (2025-01-28T02:44:31Z)
• Context-Aware Neural Gradient Mapping for Fine-Grained Instruction Processing [0.0]
  This paper introduces a dynamic gradient adjustment mechanism, incorporating contextual embeddings directly into the optimization process. The proposed framework consistently outperforms baseline models across various metrics, including accuracy, robustness to noise, and computational efficiency. The integration of context-specific embeddings allows for a more complex understanding of language, thereby improving the model's ability to handle diverse linguistic phenomena.
  arXiv  Detail & Related papers  (2025-01-24T21:49:24Z)
• Autonomous Structural Memory Manipulation for Large Language Models Using Hierarchical Embedding Augmentation [0.0]
  This study introduces hierarchical embedding augmentation as a means to redefine the representation of tokens through multi-level semantic structures. Results reveal substantial improvements in computational efficiency, with marked reductions in processing overhead for longer input sequences. The ability to dynamically adjust token representations and memory configurations contributed to the model's robustness under varied and unpredictable input conditions.
  arXiv  Detail & Related papers  (2025-01-23T22:20:36Z)
• Neural Contextual Reinforcement Framework for Logical Structure Language Generation [1.08272575635683]
  The framework integrates custom reward functions and dynamic context alignment mechanisms. It produces outputs that align closely with human expectations of logical structure and semantic flow. It exhibits robustness in handling noisy input data and scalability across varying model sizes.
  arXiv  Detail & Related papers  (2025-01-20T11:34:28Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Unified Language-driven Zero-shot Domain Adaptation [55.64088594551629]
  Unified Language-driven Zero-shot Domain Adaptation (ULDA) is a novel task setting. It enables a single model to adapt to diverse target domains without explicit domain-ID knowledge.
  arXiv  Detail & Related papers  (2024-04-10T16:44:11Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

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.