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Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction. Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates. We model the table pre-training task as a sequence generation task and pre-train ReasTAP to generate precise answers to the synthetic examples. ReasTAP is evaluated on four benchmarks covering three downstream tasks including: 1) WikiSQL and WTQ for Table Question Answering; 2) TabFact for Table Fact Verification; and 3) LogicNLG for Faithful Table-to-Text Generation. Experimental results demonstrate that ReasTAP achieves new state-of-the-art performance on all benchmarks and delivers a significant improvement on low-resource setting. Our code is publicly available at https://github.com/Yale-LILY/ReasTAP.","url_abs":"https://arxiv.org/abs/2210.12374v1","url_pdf":"https://arxiv.org/pdf/2210.12374v1.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":"reastap-injecting-table-reasoning-skills","repo_url":"https://github.com/yale-lily/reastap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"fact-verification","task_name":"Fact Verification"},{"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":"table-to-text-generation","task_name":"Table-to-Text Generation"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-parsing-on-wikisql-1","task":"Semantic Parsing","dataset":"WikiSQL","model":"ReasTAP-Large (weak supervision)","rank_in_archive_order":4,"of":5,"metrics":{"Denotation accuracy (test)":"89.2"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-wikitablequestions","task":"Semantic Parsing","dataset":"WikiTableQuestions","model":"ReasTAP-Large","rank_in_archive_order":16,"of":22,"metrics":{"Accuracy (Dev)":"59.7","Accuracy (Test)":"58.7"},"uses_additional_data":false},{"leaderboard":"/sota/table-based-fact-verification-on-tabfact","task":"Table-based Fact Verification","dataset":"TabFact","model":"ReasTAP-Large","rank_in_archive_order":7,"of":15,"metrics":{"Test":"84.9","Val":"84.6"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.12374","atlas_url":"https://app.syntology.ai/?focus=2210.12374","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12374"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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