Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation
📰 ArXiv cs.AI
Learn to generate precise Text-To-Cypher parsers using grounded knowledge graph data generation to improve conversational interfaces for property graphs
Action Steps
- Generate synthetic data using grounded knowledge graph data generation methods
- Fine-tune small LLMs using the generated data
- Evaluate the performance of the fine-tuned LLMs on Text-To-Cypher benchmarks
- Apply the fine-tuned LLMs to real-world property graph databases
- Compare the results with existing Text-To-Cypher parsers
Who Needs to Know This
Data scientists and AI engineers working on conversational interfaces for property graphs can benefit from this research to improve the accuracy of Text-To-Cypher parsers
Key Insight
💡 Grounded knowledge graph data generation can be used to fine-tune small LLMs for precise Text-To-Cypher parsing
Share This
🤖 Improve Text-To-Cypher parsers with grounded knowledge graph data generation! 📈
Key Takeaways
Learn to generate precise Text-To-Cypher parsers using grounded knowledge graph data generation to improve conversational interfaces for property graphs
Full Article
Title: Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation
Abstract:
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, dem
Abstract:
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, dem
DeepCamp AI