# Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation

arXiv: https://arxiv.org/abs/2606.14325 · HTML: https://mlsanity.com/paper/2606.14325/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Text2Cypher: Bridging Natural Language and Graph Databases](https://mlsanity.com/paper/2412.10064/) (2024)
- [SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task](https://mlsanity.com/paper/2406.10710/) (2024)
- [CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era](https://mlsanity.com/paper/2412.18702/) (2024)
- [Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL](https://mlsanity.com/paper/2402.16567/) (2024)
- [GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation](https://mlsanity.com/paper/2205.12078/) (2022)
- [The Gremlin Graph Traversal Machine and Language](https://mlsanity.com/paper/1508.03843/) (2015)
- [Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework](https://mlsanity.com/paper/2412.12612/) (2024)
- [NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language](https://mlsanity.com/paper/2412.10434/) (2024)
- [Rel2Graph: Automated Mapping From Relational Databases to a Unified Property Knowledge Graph](https://mlsanity.com/paper/2310.01080/) (2023)
- [Text2GraphQuery-Bench: A Text to Graph Query Benchmark](https://mlsanity.com/paper/2602.11745/) (2026)

## Built on by (most cited first, 1 of 1)

- [What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Framework](https://mlsanity.com/paper/2609.12746/) (2026)

## Abstract (arXiv)

Property Graphs are rapidly being adopted as database frameworks for representing heterogeneous data sources. To enable precise access to the information contained in them we need conversational interfaces based on Text-To-Cypher (Text2Cypher) parsers. This paper presents an automatic synthetic data generation method that can be leveraged to fine-tune small LLMs for this task. We conduct experiments on all the major Text-To-Cypher benchmarks, demonstrating that with our synthetic data generation approach we can significantly increase the performance of small LLMs, allowing them to compete with much larger proprietary models. This means that in settings in which models must be locally deployed we can ensure data-sovereignty without sacrificing accuracy and without costly annotation campaigns.