This study investigates the automatic identification of the English ditransitive construction by integrating LoRA-based fine-tuning of a large language model with a Retrieval-Augmented Generation (RAG) framework.A binary classification task was conducted on annotated data from the British National Corpus. Results demonstrate that a LoRA-fine-tuned Qwen3-8B model significantly outperformed both a native Qwen3-MAX model and a theory-only RAG system. Detailed error analysis reveals that fine-tuning shifts the model's judgment from a surface-form pattern matching towards a more semantically grounded understanding based.

翻译：本研究通过整合基于LoRA的大型语言模型微调与检索增强生成（RAG）框架，探索英语双及物构式的自动识别方法。基于英国国家语料库的标注数据开展了二元分类任务。实验结果表明，经过LoRA微调的Qwen3-8B模型在性能上显著优于原生Qwen3-MAX模型及纯理论RAG系统。详细的错误分析表明，微调使模型的判断机制从表层形式匹配转向更基于语义理解的认知模式。