Year: 2026
Type: conference-paper
Abstract: Despite growing interest in NL2GQL, benchmarking progress has been constrained by the lack of resources that are simultaneously largescale, cross-domain, and cross-dialect.To address this gap, we present GQLBench, a new benchmark built through an automated and scalable framework that integrates NL2SQL-to-NL2GQL conversion with graph-native data generation.GQLBench supports executionbased evaluation on both Cypher and ISO GQL, covering hundreds of graph databases and over 20k natural language questions for each dialect.By combining converted data from mature NL2SQL resources with synthetic graphspecific queries, it captures both schema diversity from real-world relational sources and graph-native reasoning challenges, including long paths and cycles.Beyond overall performance comparison, GQLBench also enables fine-grained evaluation across dialects, graph patterns, and query complexity.Experiments on advanced LLMs show that even strong proprietary models struggle on GQLBench, with gemini-3-flash achieving only 35.40% average execution accuracy across the two dialects.Our data and code are available at https://github.com/qxssadf/GQLBench.
Institutions: Fudan University, Shanghai Innovation Institute
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G2053532743
OpenAlex ID: W7166842656