@inproceedings{su-etal-2026-gqlbench,
    title = "{GQLB}ench: A Large-Scale Cross-Domain, Cross-Dialect Benchmark for {NL}2{GQL}",
    author = "Su, Yanning  and
      Zhou, Yuhang  and
      Fang, Yang  and
      Liu, Sen  and
      Ye, Guangnan  and
      Chai, Hongfeng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1476/",
    pages = "31989--32014",
    ISBN = "979-8-89176-390-6",
    abstract = "Despite growing interest in NL2GQL, benchmarking progress has been constrained by the lack of resources that are simultaneously large-scale, 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 execution-based 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 graph-specific 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."
}