While current NL2SQL tasks constructed using Foundation Models have achieved
commendable results, their direct application to Natural Language to Graph
Query Language (NL2GQL) tasks poses challenges due to the significant
differences between GQL and SQL expressions, as well as the numerous types of
GQL. Our extensive experiments reveal that in NL2GQL tasks, larger Foundation
Models demonstrate superior cross-schema generalization abilities, while
smaller Foundation Models struggle to improve their GQL generation capabilities
through fine-tuning. However, after fine-tuning, smaller models exhibit better
intent comprehension and higher grammatical accuracy. Diverging from rule-based
and slot-filling techniques, we introduce R3-NL2GQL, which employs both smaller
and larger Foundation Models as reranker, rewriter and refiner. The approach
harnesses the comprehension ability of smaller models for information reranker
and rewriter, and the exceptional generalization and generation capabilities of
larger models to transform input natural language queries and code structure
schema into any form of GQLs. Recognizing the lack of established datasets in
this nascent domain, we have created a bilingual dataset derived from graph
database documentation and some open-source Knowledge Graphs (KGs). We tested
our approach on this dataset and the experimental results showed that delivers
promising performance and robustness.Our code and dataset is available at
this https URL

当前的基础模型构建的 NL2SQL 任务在自然语言到图查询语言（NL2GQL）任务中的直接应用存在挑战，我们引入了 R3-NL2GQL 方法，使用较小和较大的基础模型作为重新排序器、重写器和细化器，同时考虑到了 GQL 的差异和较多的类型，我们的实验结果表明该方法在 NL2GQL 任务中具有产生 GQL 的优异能力、更好的意图理解和高精确度的语法。