mlsanityData to 7 Oct 2026

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

arXiv 2412.12612 ↗First posted 17 Dec 2024cs.CL cs.AI cs.IRcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

15 of 46 references.

References · 15

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
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  10. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  11. 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
  12. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
3 more
  1. 2024Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?cited by 55
  2. 2023Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustnesscited by 50
  3. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 6.

10 papers.

  1. 2025M3TQA: Massively Multilingual Multitask Table Question Answeringcited by 3
  2. 2025Apriel-1.5-15b-Thinkercited by 2
  3. 2025ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphscited by 1
  4. 2026A Human-in-the-Loop, LLM-Centered Architecture for Knowledge-Graph Question Answeringcited by 1
  5. 2026Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generationcited by 1
  6. 2025Skeletons Matter: Dynamic Data Augmentation for Text-to-Querycited by 0
  7. 2026PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systemscited by 0
  8. 2026KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systemscited by 0
  9. 2026What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Frameworkcited by 0
  10. 2026CypherTurn: A Multi-Turn Benchmark for Conversational Text-to-Cypher Evaluation and the Autonomy Divergencecited by 0