面向大语言模型的上下文保持梯度调制：长文本生成中语义一致性的新方法
Context-Preserving Gradient Modulation for Large Language Models: A Novel Approach to Semantic Consistency in Long-Form Text Generation
AI总结：
本文提出一种上下文保持的梯度调制方法，通过动态调整参数更新来提升长文本生成的语义一致性，实验证明其在连贯性、上下文保持和计算效率上均优于基线模型。
摘要：
在长文本生成中，保持扩展文本序列的语义一致性仍是一个基本挑战，传统训练方法往往难以防止上下文漂移和连贯性退化。本文引入了一种新颖的梯度调制方法，旨在根据上下文相关性动态调整参数更新，确保生成的文本与先前的论述保持一致。通过集成一个调制函数，基于学习到的上下文依赖关系选择性地放大或衰减梯度，所提方法在不增加显著计算开销的情况下增强了模型生成叙事的稳定性。与基线模型的对比评估显示，该方法在连贯性、上下文保持和长程依赖追踪方面均有改进，证明了在梯度层面修改学习过程的有效性。结果表明，句子结构多样性和词汇多样性受益于该方法，减少了重复措辞，并提高了在不同语言语境中的适应性。连贯性指标的统计验证进一步证实了观察到的改进，不一致性的显著减少直接源于调制机制。计算效率评估确认，该框架在不需对底层架构进行重大修改的情况下实现了这些增益，确保了与现有优化工作流的兼容性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
