PIPE-Cypher: Automated Benchmark Generation for Text-to-Cypher Database Query Systems
Researchers have developed PIPE-Cypher, a system that automatically generates benchmarks for evaluating text-to-Cypher translation systems on enterprise property graphs. The system addresses the challenge that enterprise databases have unique schemas and evolving structures, making it difficult to create representative test datasets. This work enables organizations to create deployment-relevant benchmarks that reflect actual user queries and improve model evaluation for database query systems.
PIPE-Cypher is a local pipeline that converts live property graphs and seed queries into balanced natural language-to-Cypher benchmarks for testing text-to-database query systems. The system combines multiple techniques including schema profiling, reverse-query grounding, constrained generation, execution validation, and a calibrated local language model judge to ensure generated benchmarks are executable, diverse, and balanced across query types and difficulty levels. The researchers demonstrated the approach by generating 3,000 validated examples and evaluating 11 downstream models using a local Qwen3.5-9B model. Key findings show that zero-shot transfer between benchmarks is weak, but few-shot learning with schema-specific examples significantly improves performance. The work transforms Text2Cypher benchmarking from a manual, one-time process into a repeatable pipeline that can evolve alongside enterprise databases and their actual usage patterns.
What's missing
The paper does not discuss potential limitations in the approach, such as how the system handles extremely large or complex schemas, whether there are failure modes in the reverse-query grounding process, or how the local LLM judge's calibration generalizes across different domain types beyond the financial and social network benchmarks tested.
What different sources said
- arXiv cs.AICenter
PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems
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