摘 要
自然语言处理领域近年来取得了显著进展，其中预训练语言模型发挥了关键作用。随着深度学习技术的发展，基于大规模语料库的预训练模型已成为自然语言理解与生成任务的核心驱动力。然而，现有模型在资源消耗、泛化能力及应用场景等方面仍存在诸多挑战。为此，本研究聚焦于探索预训练语言模型的创新路径，旨在通过引入多模态融合、知识增强及轻量化设计等方法，提升模型的性能与适用性。研究采用理论分析与实验验证相结合的方式，系统评估了不同优化策略对模型效果的影响。通过对Transformer架构进行改进，在保持计算效率的同时实现了更高的表达能力；提出基于图神经网络的知识注入机制，有效增强了模型对复杂语义的理解；设计了分层蒸馏算法，成功降低了模型参数量而不损失精度。实验结果表明，所提出的创新方案在多个基准测试中均取得显著优于传统方法的表现，特别是在低资源场景下展现出更强的适应性。本研究不仅为预训练语言模型的发展提供了新的思路，也为推动自然语言处理技术向更高效、更智能方向发展奠定了理论基础。
关键词：预训练语言模型 多模态融合 知识增强 轻量化设计
Abstract
Recent advancements in the field of natural language processing have been significant, with pre-trained language models playing a pivotal role. As deep learning technologies evolve, pre-trained models based on large-scale corpora have become the core driving force for both natural language understanding and generation tasks. However, existing models still face numerous challenges in terms of resource consumption, generalization capabilities, and application scenarios. To address these issues, this study focuses on exploring innovative approaches for pre-trained language models, aiming to enhance their performance and applicability through methods such as multimodal fusion, knowledge enhancement, and lightweight design. By combining theoretical analysis with experimental validation, the study systematically evaluates the impact of various optimization strategies on model performance. Improvements to the Transformer architecture achieve higher expressive power while maintaining computational efficiency; a graph neural network-based knowledge injection mechanism effectively enhances the model's understanding of complex semantics; and a hierarchical distillation algorithm successfully reduces the model parameter size without sacrificing accuracy. Experimental results demonstrate that the proposed innovations significantly outperform traditional methods across multiple benchmark tests, particularly exhibiting stronger adaptability in low-resource scenarios. This research not only provides new insights into the development of pre-trained language models but also lays a theoretical foundation for advancing natural language processing technology towards greater efficiency and intelligence.
Keyword:Pre-Trained Language Model Multi-Modal Fusion Knowledge Enhancement Lightweight Design
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法 2
2预训练模型架构创新 2
2.1模型架构优化路径 2
2.2新型网络结构探索 3
2.3参数高效利用策略 3
2.4架构创新案例分析 4
3数据与训练机制创新 4
3.1大规模语料库构建 4
3.2自监督学习新方法 5
3.3小样本学习技术应用 5
3.4增量训练模式探讨 6
4应用场景与性能提升 7
4.1跨领域迁移能力 7
4.2特定任务优化方案 7
4.3实时处理性能改进 8
4.4性能评估体系建立 8
结论 9
参考文献 11
致谢 12
