用微调+检索增强，让大模型更准识别英语双宾语结构。
Leveraging Lora Fine-Tuning and Knowledge Bases for Construction Identification
- 用LoRA微调Qwen3-8B，结合检索增强生成框架。
- 在英国国家语料库上分类准确率显著优于原模型和纯理论系统。
- 模型从机械匹配转向语义理解，错误分析揭示认知转变。
本研究通过将基于LoRA的大型语言模型微调与检索增强生成（RAG）框架结合，探索自动识别英语双宾语结构的方法。在英国国家语料库的标注数据上进行了二分类任务。结果表明，经LoRA微调的Qwen3-8B模型显著优于原始的Qwen3-MAX模型以及仅依赖理论的RAG系统。详细错误分析显示，微调使模型的判断从表层形式匹配转向更基于语义的理解。
原文摘要 · Abstract (English)
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.
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