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Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation, but they often struggle with large tables due to their limited input length. In this paper, we propose TabSQLify, a novel method that leverages text-to-SQL generation to decompose tables into smaller and relevant sub-tables, containing only essential information for answering questions or verifying statements, before performing the reasoning task. In our comprehensive evaluation on four challenging datasets, our approach demonstrates comparable or superior performance compared to prevailing methods reliant on full tables as input. Moreover, our method can reduce the input context length significantly, making it more scalable and efficient for large-scale table reasoning applications. Our method performs remarkably well on the WikiTQ benchmark, achieving an accuracy of 64.7%. Additionally, on the TabFact benchmark, it achieves a high accuracy of 79.5%. 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TabSQLify can reduce the table size significantly alleviating the computational load on LLMs when handling large tables without compromising performance.","url_abs":"https://arxiv.org/abs/2404.10150v1","url_pdf":"https://arxiv.org/pdf/2404.10150v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tabsqlify-enhancing-reasoning-capabilities-of","repo_url":"https://github.com/mahadi-nahid/tabsqlify","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"tabsqlify-enhancing-reasoning-capabilities-of","repo_url":"https://github.com/nikhilsab/h-star","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"table-based-fact-verification","task_name":"Table-based Fact Verification"},{"task_slug":"text-to-sql","task_name":"Text to SQL"},{"task_slug":"text-to-sql","task_name":"Text-To-SQL"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-wikisql","task":"Question Answering","dataset":"WikiSQL","model":"TabSQLify","rank_in_archive_order":2,"of":2,"metrics":{"Exact Match (EM)":"82.84"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-wikitablequestions","task":"Question Answering","dataset":"WikiTableQuestions","model":"TabSQLify (col+row)","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy (Test)":"64.7"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-wikitablequestions","task":"Semantic Parsing","dataset":"WikiTableQuestions","model":"TabSQLify (col+row)","rank_in_archive_order":12,"of":22,"metrics":{"Accuracy (Test)":"64.7"},"uses_additional_data":false},{"leaderboard":"/sota/table-based-fact-verification-on-tabfact","task":"Table-based Fact Verification","dataset":"TabFact","model":"TabSQLify (col+row)","rank_in_archive_order":12,"of":15,"metrics":{"Test":"79.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.10150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10150"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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