arXiv:2606.14325v1 Announce Type: cross 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 perfor
The rapid advancement of LLMs necessitates more efficient and precise methods for interacting with complex data structures like knowledge graphs.
Improving Text-to-Cypher interfaces enables more powerful and accessible conversational AI for enterprise and specialized data environments.
This breakthrough provides a new method for training smaller LLMs to translate natural language into query languages for graph databases, making advanced data access more feasible for a wider range of applications.
- · AI developers
- · Graph database providers
- · Enterprises with complex data
- · LLM developers
- · Manual data query specialists
Widespread adoption of AI-powered conversational interfaces for property graph databases.
Increased efficiency in data analysis and knowledge discovery across various industries.
New classes of AI agents emerge that autonomously query and reason over structured enterprise knowledge.
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Read at arXiv cs.AI