mlsanityData to 7 Oct 2026

SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task

arXiv 2406.10710 ↗First posted 15 Jun 2024cs.AI cs.CLcited by 19

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

Integrating Large Language Models (LLMs) with existing Knowledge Graph (KG) databases presents a promising avenue for enhancing LLMs' efficacy and mitigating their "hallucinations". Given that most KGs reside in graph databases accessible solely through specialized query languages (e.g., Cypher), it is critical to connect LLMs with KG databases by automating the translation of natural language into Cypher queries (termed as "Text2Cypher" task). Prior efforts tried to bolster LLMs' proficiency in Cypher generation through Supervised Fine-Tuning (SFT). However, these explorations are hindered by the lack of annotated datasets of Query-Cypher pairs, resulting from the labor-intensive and domain-specific nature of such annotation. In this study, we propose SyntheT2C, a methodology for constructing a synthetic Query-Cypher pair dataset, comprising two distinct pipelines: (1) LLM-based prompting and (2) template-filling. SyntheT2C is applied to two medical KG databases, culminating in the creation of a synthetic dataset, MedT2C. Comprehensive experiments demonstrate that the MedT2C dataset effectively enhances the performance of backbone LLMs on Text2Cypher task via SFT. Both the SyntheT2C codebase and the MedT2C dataset are released in https://github.com/ZGChung/SyntheT2C.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 20 of its 49 references that are in the map. First posted on arXiv 15 Jun 2024.

Builds on

20 of 49 references.

References · 20

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8 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 8; 2026: 9.

19 papers.

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  1. 2024Text2Cypher: Bridging Natural Language and Graph Databasescited by 30
  2. 2025Building a Foundational Guardrail for General Agentic Systems via Synthetic Datacited by 26
  3. 2024CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Eracited by 13
  4. 2025Enhancing Text2Cypher with Schema Filteringcited by 6
  5. 2025STEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Modelscited by 6
  6. 2026Synthetic Interaction Data for Scalable Personalization in Large Language Modelscited by 4
  7. 2025Neuro Symbolic Knowledge Reasoning for Procedural Video Question Answeringcited by 3
  8. 2025ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructionscited by 3
  9. 2026Text2GraphQuery-Bench: A Text to Graph Query Benchmarkcited by 3
  10. 2025ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphscited by 1
  11. 2026GraphWalk: Enabling Reasoning in Large Language Models through Tool-Based Graph Navigationcited by 1
  12. 2026CYGNET: Cypher Gate for Neural Execution Triage and Cost Containmentcited by 1
7 more
  1. 2026Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generationcited by 1
  2. 2026Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuningcited by 1
  3. 2025Natural Language Interaction with Databases on Edge Devices in the Internet of Battlefield Thingscited by 0
  4. 2025Skeletons Matter: Dynamic Data Augmentation for Text-to-Querycited by 0
  5. 2026PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systemscited by 0
  6. 2026SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilitiescited by 0
  7. 2026CypherTurn: A Multi-Turn Benchmark for Conversational Text-to-Cypher Evaluation and the Autonomy Divergencecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
  2. 2024Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL
  3. 2025InvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generation
  4. 2025Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models
  5. 2024Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
  6. 2024Towards Evaluating Large Language Models for Graph Query Generation
  7. 2023Large Language Models on Graphs: A Comprehensive Survey
  8. 2025Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement