Context-Preserving Gradient Modulation for Large Language Models: A Novel Approach to Semantic Consistency in Long-Form Text Generation

TL;DR

This paper introduces a gradient modulation technique for large language models that dynamically adjusts parameter updates to improve semantic consistency and coherence in long-form text generation, addressing the challenge of contextual drift.

Contribution

It presents a novel gradient modulation method that enhances semantic coherence in long-form text generation without significant computational overhead.

Findings

Improved coherence and contextual retention in generated texts

Reduction in repetitive phrasing and increased lexical diversity

Enhanced long-range dependency tracking and narrative stability

Abstract

Maintaining semantic consistency over extended text sequences remains a fundamental challenge in long-form text generation, where conventional training methodologies often struggle to prevent contextual drift and coherence degradation. A novel gradient modulation approach is introduced, designed to adjust parameter updates dynamically in response to contextual relevance, ensuring that generated text remains aligned with prior discourse. By integrating a modulation function that selectively amplifies or attenuates gradients based on learned contextual dependencies, the proposed method enhances the stability of model-generated narratives without imposing significant computational overhead. Comparative evaluations against baseline models reveal improvements in coherence, contextual retention, and long-range dependency tracking, demonstrating the effectiveness of modifying the learning process at the gradient level. The results indicate that sentence structure variability and lexical diversity benefit from this approach, mitigating repetitive phrasing and improving adaptability across diverse linguistic contexts. Statistical validation of coherence metrics further substantiates the observed enhancements, with a significant reduction in inconsistencies emerging as a direct consequence of the modulation mechanism. Computational efficiency assessments confirm that the framework achieves these gains without requiring substantial modifications to the underlying architecture, ensuring compatibility with existing optimization workflows.