Transformer模型在自然语言处理中的迁移学习效果分析

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中图分类号：TP391 文献标识码：A文章编号:1672-3791(2026)03-0044-03

Analysis of the TransferLearning Effectof Transformer Model in Natural Language Processing

LI Yang

Jiangsu Jurong Secondary Professonal School,Jurong,Jiangsu Province,2124Oo China

Abstract: Based on the performance of Transformer models in Natural Language Procesing (NLP) transfer learning,this article focusesonanalyzing the corrlationbetweenarchitecture characteristicsand task adaptability.To systematically evaluate the efects of encoding decoding path configuration,parameter transfer hierarchy,and attntion mechanism adjustment strategies on transfer performance through multi task controlled experiments.The transfer efficiencyof Transformer mainlydependsonitsabilityto reconstructatention distributionandconsistency inhierarchicalrepresentation.Analysis shows that it performs outstandinglyin component reuse andarchitecture robustness. Theresearch results provide a theoretical basis and technical guidance for the architecture improvement and transfer method optimization of pre trained models.

Keywords: Transformer model; Natural language processing; Transfer learning; Structural adaptation

本文聚焦Transformer模型在自然语言处理中的结构演化与迁移学习机制，依托预训练语言模型在多任务环境下的广泛应用背景，探讨模型结构对任务适应性的影响路径。（剩余4351字）