@inproceedings{khoja-etal-2025-weaver,
title = "Weaver: Interweaving {SQL} and {LLM} for Table Reasoning",
author = "Khoja, Rohit and
Gupta, Devanshu and
Fu, Yanjie and
Roth, Dan and
Gupta, Vivek",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1436/",
doi = "10.18653/v1/2025.emnlp-main.1436",
pages = "28282--28308",
ISBN = "979-8-89176-332-6",
abstract = "Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggles to process, especially for tasks requiring semantic reasoning. While Large Language Models (LLMs) excel at understanding context, they face limitations with long input sequences. Existing approaches that combine SQL and LLM typically rely on rigid, predefined workflows, limiting their adaptability to complex queries. To address these issues, we introduce Weaver, a modular pipeline that dynamically integrates SQL and LLM for table-based question answering (Table QA). Weaver generates a flexible, step-by-step plan that combines SQL for structured data retrieval with LLMs for semantic processing. By decomposing complex queries into manageable subtasks, Weaver improves accuracy and generalization. Our experiments show that consistently outperforms state-of-the-art methods across four Table QA datasets, reducing both API calls and error rates."
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<abstract>Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggles to process, especially for tasks requiring semantic reasoning. While Large Language Models (LLMs) excel at understanding context, they face limitations with long input sequences. Existing approaches that combine SQL and LLM typically rely on rigid, predefined workflows, limiting their adaptability to complex queries. To address these issues, we introduce Weaver, a modular pipeline that dynamically integrates SQL and LLM for table-based question answering (Table QA). Weaver generates a flexible, step-by-step plan that combines SQL for structured data retrieval with LLMs for semantic processing. By decomposing complex queries into manageable subtasks, Weaver improves accuracy and generalization. Our experiments show that consistently outperforms state-of-the-art methods across four Table QA datasets, reducing both API calls and error rates.</abstract>
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%0 Conference Proceedings
%T Weaver: Interweaving SQL and LLM for Table Reasoning
%A Khoja, Rohit
%A Gupta, Devanshu
%A Fu, Yanjie
%A Roth, Dan
%A Gupta, Vivek
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F khoja-etal-2025-weaver
%X Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggles to process, especially for tasks requiring semantic reasoning. While Large Language Models (LLMs) excel at understanding context, they face limitations with long input sequences. Existing approaches that combine SQL and LLM typically rely on rigid, predefined workflows, limiting their adaptability to complex queries. To address these issues, we introduce Weaver, a modular pipeline that dynamically integrates SQL and LLM for table-based question answering (Table QA). Weaver generates a flexible, step-by-step plan that combines SQL for structured data retrieval with LLMs for semantic processing. By decomposing complex queries into manageable subtasks, Weaver improves accuracy and generalization. Our experiments show that consistently outperforms state-of-the-art methods across four Table QA datasets, reducing both API calls and error rates.
%R 10.18653/v1/2025.emnlp-main.1436
%U https://aclanthology.org/2025.emnlp-main.1436/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1436
%P 28282-28308
Markdown (Informal)
[Weaver: Interweaving SQL and LLM for Table Reasoning](https://aclanthology.org/2025.emnlp-main.1436/) (Khoja et al., EMNLP 2025)
ACL
- Rohit Khoja, Devanshu Gupta, Yanjie Fu, Dan Roth, and Vivek Gupta. 2025. Weaver: Interweaving SQL and LLM for Table Reasoning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 28282–28308, Suzhou, China. Association for Computational Linguistics.