RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation
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 learning (ICL). We compare two inference methods: Independent Scaling (IS), which performs memoryless resampling, and Reflection-Augmented Scaling...
| Authors | Minseok Jung, Abhas Ricky, Muhammad Rameez Chatni |
|---|---|
| Primary category | cs.CL |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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