Summary of Synthet2c: Generating Synthetic Data For Fine-tuning Large Language Models on the Text2cypher Task, by Ziije Zhong et al.
SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task
by Ziije Zhong, Linqing Zhong, Zhaoze Sun, Qingyun Jin, Zengchang Qin, Xiaofan Zhang
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology, SyntheT2C, aims to construct a synthetic Query-Cypher pair dataset for enhancing the Text2Cypher task. This task involves translating natural language into Cypher queries for querying knowledge graph (KG) databases. The study proposes two pipelines: LLM-based prompting and template-filling. The methodology is applied to medical KG databases, resulting in the creation of a synthetic dataset, MedT2C. Experimental results show that the MedT2C dataset effectively improves the performance of backbone Large Language Models (LLMs) on the Text2Cypher task through Supervised Fine-Tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SyntheT2C is a new way to make computers talk to special databases called knowledge graphs. Right now, these databases are hard to communicate with because they only understand a special language called Cypher. To fix this, scientists tried training computer models using labeled data, but that’s very time-consuming and expensive. In this study, researchers created a new method called SyntheT2C, which uses two steps: asking the computer model questions and filling in templates. They tested this on medical databases and found it works well. The code and dataset are available for other scientists to use. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge graph » Prompting » Supervised
