A multi-agent LLM framework with schema extraction, fine-tuned query generation, and execution-error feedback outperforms prior NL2GQL systems on both a new nGQL dataset and the existing SpCQL benchmark.
An empirical study on recent graph database systems,
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NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language
A multi-agent LLM framework with schema extraction, fine-tuned query generation, and execution-error feedback outperforms prior NL2GQL systems on both a new nGQL dataset and the existing SpCQL benchmark.