Graph Databases (Graph DB) are widely applied in various fields, including
finance, social networks, and medicine. However, translating Natural Language
(NL) into the Graph Query Language (GQL), commonly known as NL2GQL, proves to
be challenging due to its inherent complexity and specialized nature. Some
approaches have sought to utilize Large Language Models (LLMs) to address
analogous tasks like text2SQL. Nevertheless, when it comes to NL2GQL taskson a
particular domain, the absence of domain-specific NL-GQL data pairs makes it
difficult to establish alignment between LLMs and the graph DB. To address this
challenge, we propose a well-defined pipeline. Specifically, we utilize ChatGPT
to create NL-GQL data pairs based on the given graph DB with self-instruct.
Then, we use the created data to fine-tune LLMs, thereby achieving alignment
between LLMs and the graph DB. Additionally, during inference, we propose a
method that extracts relevant schema to the queried NL as the input context to
guide LLMs for generating accurate GQLs.We evaluate our method on two
constructed datasets deriving from graph DBs in finance domain and medicine
domain, namely FinGQL and MediGQL. Experimental results demonstrate that our
method significantly outperforms a set of baseline methods, with improvements
of 5.90 and 6.36 absolute points on EM, and 6.00 and 7.09 absolute points on
EX, respectively.

使用 ChatGPT 创建自我指导的领域特定自然语言 - 图查询数据对以及使用这些数据对大型语言模型进行微调，并提出了一种提取相关模式的方法来引导模型生成准确的图查询语言（GQL）。实验证明，该方法在金融领域和医学领域的图数据库上取得了显著的性能改进。

将大型语言模型对齐到领域特定的图数据库

Aligning Large Language Models to a Domain-specific Graph Database

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。