Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation
For organizations needing locally deployed, data-sovereign conversational interfaces to property graphs, this method reduces reliance on costly annotation and large proprietary models.
The paper presents an automatic synthetic data generation method for fine-tuning small LLMs to perform Text-To-Cypher parsing on property graphs. The method significantly boosts small LLM performance on major benchmarks, enabling them to compete with larger proprietary models while preserving data sovereignty.
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.