@inproceedings{soliman-etal-2026-core,
title = "{CORE}-{T}: {CO}herent {RE}trieval of Tables for Text-to-{SQL}",
author = "Soliman, Hassan and
Gupta, Vivek and
Roth, Dan and
Gurevych, Iryna",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.89/",
pages = "2082--2114",
ISBN = "979-8-89176-459-0",
abstract = "Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes a key bottleneck for end-to-end performance. We study an open-book setting where queries must be answered over large, heterogeneous table collections pooled from many sources, without clean scoping signals such as database identifiers. Here, dense retrieval (DR) achieves high recall but returns many distractors, while join-aware alternatives often rely on extra assumptions and/or incur high inference overhead. We propose CORE-T, a scalable, training-free framework that enriches tables with LLM-generated purpose metadata and pre-computes a lightweight table-compatibility cache. At inference time, DR returns top-K candidates; a single LLM call selects a coherent, joinable subset, and a two-step additive adjustment stage restores strongly compatible tables. Across Bird, Spider, MMQA, and Beaver, CORE-T improves over DR by up to 22.7 points in table-selection F1 while returning up to 40{\%} fewer tables, and by up to 24.4 points in multi-table execution accuracy, and uses 1.64{--}4.20{\texttimes} fewer total selection tokens than LLM-intensive baselines."
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<abstract>Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes a key bottleneck for end-to-end performance. We study an open-book setting where queries must be answered over large, heterogeneous table collections pooled from many sources, without clean scoping signals such as database identifiers. Here, dense retrieval (DR) achieves high recall but returns many distractors, while join-aware alternatives often rely on extra assumptions and/or incur high inference overhead. We propose CORE-T, a scalable, training-free framework that enriches tables with LLM-generated purpose metadata and pre-computes a lightweight table-compatibility cache. At inference time, DR returns top-K candidates; a single LLM call selects a coherent, joinable subset, and a two-step additive adjustment stage restores strongly compatible tables. Across Bird, Spider, MMQA, and Beaver, CORE-T improves over DR by up to 22.7 points in table-selection F1 while returning up to 40% fewer tables, and by up to 24.4 points in multi-table execution accuracy, and uses 1.64–4.20× fewer total selection tokens than LLM-intensive baselines.</abstract>
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%0 Conference Proceedings
%T CORE-T: COherent REtrieval of Tables for Text-to-SQL
%A Soliman, Hassan
%A Gupta, Vivek
%A Roth, Dan
%A Gurevych, Iryna
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F soliman-etal-2026-core
%X Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes a key bottleneck for end-to-end performance. We study an open-book setting where queries must be answered over large, heterogeneous table collections pooled from many sources, without clean scoping signals such as database identifiers. Here, dense retrieval (DR) achieves high recall but returns many distractors, while join-aware alternatives often rely on extra assumptions and/or incur high inference overhead. We propose CORE-T, a scalable, training-free framework that enriches tables with LLM-generated purpose metadata and pre-computes a lightweight table-compatibility cache. At inference time, DR returns top-K candidates; a single LLM call selects a coherent, joinable subset, and a two-step additive adjustment stage restores strongly compatible tables. Across Bird, Spider, MMQA, and Beaver, CORE-T improves over DR by up to 22.7 points in table-selection F1 while returning up to 40% fewer tables, and by up to 24.4 points in multi-table execution accuracy, and uses 1.64–4.20× fewer total selection tokens than LLM-intensive baselines.
%U https://aclanthology.org/2026.emnlp-main.89/
%P 2082-2114
Markdown (Informal)
[CORE-T: COherent REtrieval of Tables for Text-to-SQL](https://aclanthology.org/2026.emnlp-main.89/) (Soliman et al., EMNLP 2026)
ACL
- Hassan Soliman, Vivek Gupta, Dan Roth, and Iryna Gurevych. 2026. CORE-T: COherent REtrieval of Tables for Text-to-SQL. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 2082–2114, Budapest, Hungary. Association for Computational Linguistics.