Integrating Large Language Models (LLMs) with existing Knowledge Graph (KG)
databases presents a promising avenue for enhancing LLMs' efficacy and
mitigating their "hallucinations". Given that most KGs reside in graph
databases accessible solely through specialized query languages (e.g., Cypher),
there exists a critical need to bridge the divide between LLMs and KG databases
by automating the translation of natural language into Cypher queries (commonly
termed the "Text2Cypher" task). Prior efforts tried to bolster LLMs'
proficiency in Cypher generation through Supervised Fine-Tuning. However, these
explorations are hindered by the lack of annotated datasets of Query-Cypher
pairs, resulting from the labor-intensive and domain-specific nature of
annotating such datasets. In this study, we propose SyntheT2C, a methodology
for constructing a synthetic Query-Cypher pair dataset, comprising two distinct
pipelines: (1) LLM-based prompting and (2) template-filling. SyntheT2C
facilitates the generation of extensive Query-Cypher pairs with values sampled
from an underlying Neo4j graph database. Subsequently, SyntheT2C is applied to
two medical databases, culminating in the creation of a synthetic dataset,
MedT2C. Comprehensive experiments demonstrate that the MedT2C dataset
effectively enhances the performance of backbone LLMs on the Text2Cypher task.
Both the SyntheT2C codebase and the MedT2C dataset will be released soon.

综合大型语言模型（LLMs）与现有知识图谱（KG）数据库为一体，旨在增强 LLM 的效力和减轻其 “幻觉”。本研究提出 SyntheT2C 方法，通过基于 LLM 的提示和模板填充的两个不同流程，构建了一个合成的查询 - Cypher 对数据集，以样本值采样自底层 Neo4j 图数据库。实验证明，MedT2C 数据集有效提升了主干 LLM 在 Text2Cypher 任务上的性能。