@inproceedings{pei-etal-2026-rose,
    title = "{ROSE}: An Intent-Centered Evaluation Metric for {NL}2{SQL}",
    author = "Pei, Wenqi  and
      Hou, Shizheng  and
      Li, Boyan  and
      Han, Chen  and
      Shi, Zhichao  and
      Luo, Yuyu",
    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.257/",
    pages = "5682--5709",
    ISBN = "979-8-89176-390-6",
    abstract = "Execution Accuracy (EX), the widely used metric for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions, is becoming increasingly unreliable. It is sensitive to syntactic variation, ignores that questions may admit multiple interpretations, and is easily misled by erroneous ground-truth SQL. To address this, we introduce **ROSE**, an intent-centered metric that focuses on whether the predicted SQL answers the question, rather than consistency with the ground-truth SQL under the reference-dependent paradigm. ROSE employs an adversarial Prover-Refuter cascade: SQL Prover assesses the semantic correctness of a predicted SQL against the user{'}s intent independently, while Adversarial Refuter uses the ground-truth SQL as evidence to challenge and refine this judgment. On our expert-aligned validation set **ROSE-VEC**, ROSE achieves the best agreement with human experts, outperforming the next-best metric by nearly 24{\%} in Cohen{'}s Kappa. We also conduct a large-scale re-evaluation of 19 NL2SQL methods, revealing four valuable insights. We release ROSE and ROSE-VEC to facilitate more reliable NL2SQL research."
}