多尺度特征空间中大型语言模型泛化的上下文梯度流建模
Contextual Gradient Flow Modeling for Large Language Model Generalization in Multi-Scale Feature Spaces
AI总结：
本文提出结构化梯度细化框架，通过多尺度上下文调整和动态加权策略优化大型语言模型训练，减少梯度振荡，增强层次化表征学习与跨域泛化能力。
摘要：
训练大规模神经架构的优化方法通常依赖于统一的梯度传播机制，这些机制无法与层次化语言结构对齐，限制了其在多样化语言分布上的泛化能力。本文引入了一种结构化的梯度细化框架，以纳入多尺度上下文调整，通过动态加权策略改进参数自适应，从而增强表征连贯性。实证评估表明，结构化传播机制有助于减少梯度振荡，从而带来更稳定的训练动态和更高的优化效率。对比性能评估显示，采用层次化传播策略的模型在长距离依赖保持和跨领域适应方面表现出更强的鲁棒性。权重更新的层次化调整为传统反向传播提供了一种替代方案，降低了对初始化条件的敏感性，同时提高了整体收敛效率。实验结果证实，结构化梯度传播影响了表征学习轨迹，使参数更新与更广泛的语言依赖而非孤立的词元级关系对齐。统计评估表明，结构化优化策略在保持对不同异构文本分布适应性的同时缓解了过拟合。研究结果确立了结构化梯度传播为细化层次化表征学习提供了一个经验验证的框架，支持将语言依赖更有效地整合到优化动态中。
英文摘要：
Optimization methodologies for training large-scale neural architectures often rely on uniform gradient propagation mechanisms that fail to align with hierarchical linguistic structures, limiting their capacity to generalize across diverse language distributions. A structured gradient refinement framework was introduced to incorporate multi-scale contextual adjustments, improving parameter adaptation through dynamic weighting strategies that enhanced representation coherence. Empirical evaluations demonstrated that structured propagation mechanisms contributed to reductions in gradient oscillations, resulting in more stable training dynamics and improved optimization efficiency. The comparative performance assessment indicated that models incorporating hierarchical propagation strategies exhibited greater robustness in long-range dependency retention and cross-domain adaptation. The hierarchical adjustment of weight updates provided an alternative to conventional backpropagation, reducing sensitivity to initialization conditions while improving overall convergence efficiency. The experimental results confirmed that structured gradient propagation influenced representation learning trajectories, aligning parameter updates with broader linguistic dependencies rather than isolated token-level relationships. Statistical evaluations indicated that structured optimization strategies mitigated overfitting while preserving adaptability across heterogeneous text distributions. The findings established that structured gradient propagation provided an empirically validated framework for refining hierarchical representation learning, supporting more effective integration of linguistic dependencies into optimization dynamics.
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。
