Cross-domain constituency parsing remains a challenging task due to the lack of high-quality out-of-domain data. In this paper, we propose a data augmentation method via lightweight large language model (LLM) generation and tree hybridization. We utilize LLM to generate phrase structures (subtrees) for the target domain by incorporating grammar rules and lexical head information into the prompt. To better leverage LLM-generated target-domain subtrees, we hybridize them with existing source-domain subtrees to efficiently produce a large number of structurally diverse instances. Experimental results demonstrate that our method achieves significant improvements on five target domains with a lightweight LLM generation cost.

Large language models (LLMs) have achieved remarkable success across various natural language processing (NLP) tasks. However, recent studies suggest that they still face challenges in performing fundamental NLP tasks essential for deep language understanding, particularly syntactic parsing. In this paper, we conduct an in-depth analysis of LLM parsing capabilities, delving into the underlying causes of why LLMs struggle with this task and the specific shortcomings they exhibit. We find that LLMs may be limited in their ability to fully leverage grammar rules from existing treebanks, restricting their capability to generate syntactic structures. To help LLMs acquire knowledge without additional training, we propose a self-correction method that leverages grammar rules from existing treebanks to guide LLMs in correcting previous errors. Specifically, we automatically detect potential errors and dynamically search for relevant rules, offering hints and examples to guide LLMs in making corrections themselves. Experimental results on three datasets using various LLMs demonstrate that our method significantly improves performance in both in-domain and cross-domain settings.