← 研踪 PaperTrailarXiv:2611.12731 · v1
NIFT-SQL: Customer-Supervised Table Scoring for Enterprise Text-to-SQL Retrieval
首次提交 2026-11-10 01:18
版本更新 2026-11-10 01:18
雷达观测 2026-11-10 15:22
摘要
Enterprise text-to-SQL starts with finding the tables a question needs, which is hard when a schema spans several databases and hundreds of cryptic tables. We ask whether models trained separately on each customer's example questions, reading only table names, column names, and declared keys, improve table retrieval. NIFT-SQL (Necessity-Informed Fusion for Text-to-SQL) fuses hybrid search over a retriever fine-tuned on the customer's questions with a fine-tuned 560M cross-encoder that scores every table for whether the query needs it, through a logistic regression fitted on held-out questions. On BEAVER, LiveSQLBench, and LiveSQLBench-Large (244 to 971 tables; four splits, three trainings), NIFT-SQL places every reference table in the top 15 for 69.2%, 89.5%, and 85.6% of test questions, against 30.4%, 41.0%, and 21.8% for hybrid search and 61.1%, 81.7%, and 70.4% for the fine-tuned retriever with the same labels. With off-the-shelf search, the cross-encoder without fine-tuning is 21 points lower. A zero-shot 8B reranker needs no labels and, routed by database, shows no resolved complete@15 difference on two collections; adding its scores as two fusion features gives a high-accuracy mode that reaches 72.4%, 92.5%, and 88.6%, ahead of the validation-selected zero-shot reranker variant on two collections, with no resolved difference on the third. Rank fusion of the two trained parts, grouped by database, shows no resolved difference from the learned fusion. Scored once on a newer BEAVER release, NIFT-SQL beats search and leads the label-matched retriever at top 8 on the synthetic test, but trails it at top 15 on real questions.
作者备注：41 pages, 2 figures
观测到的版本
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v1 · 2026-11-10 01:18 · 最新观测版本
NIFT-SQL: Customer-Supervised Table Scoring for Enterprise Text-to-SQL Retrieval
Enterprise text-to-SQL starts with finding the tables a question needs, which is hard when a schema spans several databases and hundreds of cryptic tables. We ask whether models trained separately on each customer's example questions, reading only table names, column names, and declared keys, improve table retrieval. NIFT-SQL (Necessity-Informed Fusion for Text-to-SQL) fuses hybrid search over a retriever fine-tuned on the customer's questions with a fine-tuned 560M cross-encoder that scores every table for whether the query needs it, through a logistic regression fitted on held-out questions. On BEAVER, LiveSQLBench, and LiveSQLBench-Large (244 to 971 tables; four splits, three trainings), NIFT-SQL places every reference table in the top 15 for 69.2%, 89.5%, and 85.6% of test questions, against 30.4%, 41.0%, and 21.8% for hybrid search and 61.1%, 81.7%, and 70.4% for the fine-tuned retriever with the same labels. With off-the-shelf search, the cross-encoder without fine-tuning is 21 points lower. A zero-shot 8B reranker needs no labels and, routed by database, shows no resolved complete@15 difference on two collections; adding its scores as two fusion features gives a high-accuracy mode that reaches 72.4%, 92.5%, and 88.6%, ahead of the validation-selected zero-shot reranker variant on two collections, with no resolved difference on the third. Rank fusion of the two trained parts, grouped by database, shows no resolved difference from the learned fusion. Scored once on a newer BEAVER release, NIFT-SQL beats search and leads the label-matched retriever at top 8 on the synthetic test, but trails it at top 15 on real questions.
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