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Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation

2026-06-12 10:08:20
Francesco Cazzaro, Jessica Lennon, Ariadna Quattoni
arXiv_AI
         
Abstract

Property Graphs are rapidly being adopted as database frameworks for representing heterogeneous data sources. To enable precise access to the information contained in them we need conversational interfaces based on Text-To-Cypher (Text2Cypher) parsers. This paper presents an automatic synthetic data generation method that can be leveraged to fine-tune small LLMs for this task. We conduct experiments on all the major Text-To-Cypher benchmarks, demonstrating that with our synthetic data generation approach we can significantly increase the performance of small LLMs, allowing them to compete with much larger proprietary models. This means that in settings in which models must be locally deployed we can ensure data-sovereignty without sacrificing accuracy and without costly annotation campaigns.

Abstract (translated)

URL

https://arxiv.org/abs/2606.14325

PDF

https://arxiv.org/pdf/2606.14325.pdf

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