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.
@article{arxiv.2601.13105,
title = {Leveraging Lora Fine-Tuning and Knowledge Bases for Construction Identification},
author = {Liu Kaipeng and Wu Ling},
journal= {arXiv preprint arXiv:2601.13105},
year = {2026}
}