PIPE-Cypher: Automated Benchmark Generation for Text-to-Cypher Database Query Systems
Researchers have introduced PIPE-Cypher, an automated pipeline that generates customized benchmarks for evaluating systems that convert natural language queries into Cypher graph database queries. Enterprise property graphs differ significantly in schema, terminology, and domain assumptions, making generic benchmarks inadequate for real-world deployment evaluation. The tool addresses a gap in the field by producing benchmarks that evolve alongside a live graph and its actual user workloads.
PIPE-Cypher is a locally-run benchmark generation pipeline designed to help enterprises evaluate Text-to-Cypher systems — tools that translate natural language questions into Cypher, the query language for property graph databases. Because enterprise graphs vary widely in structure, internal terminology, and governance constraints, off-the-shelf benchmarks often fail to reflect the queries real users and automated agents actually pose. The pipeline ingests a live property graph along with optional seed queries drawn from customer questions, analyst logs, or agent tool calls, and produces balanced, executable NL-to-Cypher benchmark sets. It incorporates schema profiling, reverse-query grounding, constrained generation, execution validation, diversity controls, and a calibrated local LLM judge using Qwen3.5-9B. In a demonstration using FinBench and SNB datasets, the system exported 3,000 accepted examples and evaluated 11 downstream models, finding that zero-shot transfer performs poorly while few-shot approaches with schema-specific example banks offer meaningful improvement. The authors position PIPE-Cypher as a repeatable process that can be updated as graphs, users, and workloads change over time.
What's missing
The paper does not appear to address computational cost or time requirements for running the full pipeline on very large enterprise graphs, nor does it discuss how sensitive benchmark quality is to the quality and representativeness of the optional seed queries provided. The study also does not report inter-annotator agreement statistics for the human labels used to calibrate the LLM judge.
What different sources said
- arXiv cs.AICenter
PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems
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