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 "name": "BIRD-Spider-Ent",
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 "last_modified": "2026-10-29T07:42:11.000Z",
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 "citation": "@inproceedings{ji-etal-2026-text,\n    title = \"Text-to-{SQL} Benchmarks Toward Enterprise Realities: Under Massive Scopes, Complex Schemas and Scattered Knowledge\",\n    author = \"Ji, Yuchen  and\n      Yu, Chengxi  and\n      Shi, Jie  and\n      Zhou, Weixin  and\n      Cao, Xi  and\n      Liang, Jiaqing  and\n      Chen, Hai  and\n      Mao, Yu  and\n      Yang, Deqing  and\n      Xiao, Yanghua\",\n    editor = \"Lee, Hung-Yi  and\n      Dev, Sunipa  and\n      Li, Jessy  and\n      Elliott, Desmond\",\n    booktitle = \"Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026\",\n    month = oct,\n    year = \"2026\",\n    address = \"Budapest, Hungary\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2026.findings-emnlp.1971/\",\n    pages = \"42028--42060\",\n    ISBN = \"979-8-89176-462-0\",\n    abstract = \"Existing Text-to-SQL benchmarks remain idealized compared with enterprise scenarios, which require massive query scopes, complex schemas, and scattered external knowledge. We present BIRD-Ent and Spider-Ent, two enterprise-oriented benchmarks constructed via a refinement framework, together with Dual-Retrieval-Augmented-Generation (DRAG) Text-to-SQL, which formalizes table-schema and knowledge retrieval before SQL generation. The benchmarks feature over 4,000 columns per query and, for BIRD-Ent, documents totaling 1.5M tokens. Evaluation on state-of-the-art models shows sharp drops, with only 39.1{\\%} EX on BIRD-Ent and 60.5{\\%} ",
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