Summary of Nat-nl2gql: a Novel Multi-agent Framework For Translating Natural Language to Graph Query Language, by Yuanyuan Liang et al.
NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language
by Yuanyuan Liang, Tingyu Xie, Gan Peng, Zihao Huang, Yunshi Lan, Weining Qian
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 This paper proposes a novel multi-agent framework called NAT-NL2GQL for translating natural language to graph query language. The framework consists of three agents: the Preprocessor agent, the Generator agent, and the Refiner agent. The Preprocessor agent manages data processing as context, while the Generator agent is a fine-tuned Large Language Model (LLM) trained on NL-GQL data. The Refiner agent refines GQL or context using error information obtained from GQL execution results. To evaluate the framework, the authors developed StockGQL, a high-quality open-source dataset constructed from a financial market graph database. Experimental results show that NAT-NL2GQL significantly outperforms baseline approaches on both the StockGQL and SpCQL datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special machines called Large Language Models to help us understand and work with big databases that are not just lists of information, but also have connections between different pieces. The researchers developed a new way to use these machines to turn natural language into the right language for working with these databases. They tested their approach on some real-world data from financial markets and found it worked much better than other methods. |
Keywords
» Artificial intelligence » Large language model
