RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation
arXiv:2605.22937v1 Announce Type: new Abstract: Inference-time scaling can reduce errors in structured query generation, but methods to allocate the compute for query code generation remains underexplored. We study Text2Cypher, where language models generate Cypher queries that execute against property graph databases. Non-executable queries constitute a distinct syntactic failure separate from semantic inaccuracy: a syntax error triggers a system-generated error message from the database. These error messages are typically discarded at inference time rather than leveraged through in-context l
The increasing complexity of AI tasks, particularly in structured data interaction, demands more robust and efficient methods for error correction in query generation.
Improving the reliability and accuracy of AI-generated queries reduces operational overhead and enhances the utility of language models in database interaction and automation.
The explicit leveraging of system-generated error messages for in-context learning provides a practical pathway to more reliable executable code generation for databases.
- · AI developers
- · Database administrators
- · Businesses leveraging LLMs for data interaction
- · Debugging tool vendors (if integrated into LLMs)
- · Manual query writers (for repetitive tasks)
AI models will become more proficient at generating executable code, particularly for database interaction.
This improved reliability can accelerate the adoption of AI agents for complex data management and automation tasks.
Reduced human intervention in database querying could lead to workforce shifts at the intersection of data engineering and AI operations.
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Read at arXiv cs.CL