Graph databases like Neo4j are gaining popularity for handling complex, interconnected data, over traditional relational databases in modeling and querying relationships. While translating natural language into SQL queries is well-researched, generating Cypher queries for Neo4j remains relatively underexplored. In this work, we present an automated, LLM-Supervised, pipeline to generate high-quality synthetic data for Text2Cypher. Our Cypher data generation pipeline introduces LLM-As-Database-Filler, a novel strategy for ensuring Cypher query correctness, thus resulting in high quality generations. Using our pipeline, we generate high quality Text2Cypher data - SynthCypher containing 29.8k instances across various domains and queries with varying complexities. Training open-source LLMs like LLaMa-3.1-8B, Mistral-7B, and QWEN-7B on SynthCypher results in performance gains of up to 40% on the Text2Cypher test split and 30% on the SPIDER benchmark, adapted for graph databases.
@article{arxiv.2412.12612,
title = {Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework},
author = {Aman Tiwari and Shiva Krishna Reddy Malay and Vikas Yadav and Masoud Hashemi and Sathwik Tejaswi Madhusudhan},
journal= {arXiv preprint arXiv:2412.12612},
year = {2025}
}