Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework

• URL: http://arxiv.org/abs/2412.12612v2
• Date: Fri, 24 Jan 2025 05:52:51 GMT
• Title: Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework
• Abstract summary: We present an automated, LLM-Supervised, pipeline to generate high-quality synthetic data for Text2Cypher.<n>Our Cypher data generation pipeline introduces LLM-As-Database-Filler, a novel strategy for ensuring Cypher query correctness.<n>Using our pipeline, we generate high quality Text2Cypher data - SynthCypher containing 29.8k instances across various domains and queries with varying complexities.
• Score: 4.080333216826685
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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