{"id":"https://openalex.org/W4414586680","doi":"https://doi.org/10.18653/v1/2026.findings-acl.759","title":"TabularMath: Understanding Math Reasoning over Tables with Large Language Models","display_name":"TabularMath: Understanding Math Reasoning over Tables with Large Language Models","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4414586680","doi":"https://doi.org/10.18653/v1/2026.findings-acl.759"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.759","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.759","pdf_url":"https://aclanthology.org/2026.findings-acl.759.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.759.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064177498","display_name":"Shiyu Tian","orcid":"https://orcid.org/0009-0000-8620-0764"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi-Yu Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101040069","display_name":"Zhi Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhi Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048336766","display_name":"Wei Dong","orcid":"https://orcid.org/0000-0002-0728-2886"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Dong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050470756","display_name":"Kun-Yang Yu","orcid":"https://orcid.org/0000-0003-0862-198X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun-Yang Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418319","display_name":"Ming\u2013Hsuan Yang","orcid":"https://orcid.org/0000-0003-4848-2304"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zi-Jian Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zi-Jian Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047808444","display_name":"Lan-Zhe Guo","orcid":"https://orcid.org/0000-0001-8965-1288"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lan-Zhe Guo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355152","display_name":"Yu-Feng Li","orcid":"https://orcid.org/0000-0002-7727-4304"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu-Feng Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00702477,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"15469","last_page":"15498"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9613999724388123,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9613999724388123,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9404000043869019,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.5738999843597412},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5065000057220459},{"id":"https://openalex.org/keywords/qualitative-reasoning","display_name":"Qualitative reasoning","score":0.4832000136375427},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.444599986076355},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.4011000096797943},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.37070000171661377},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.36579999327659607},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3449000120162964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.670199990272522},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.5738999843597412},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5065000057220459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.487199991941452},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.37869998812675476},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.36579999327659607},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34709998965263367},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33889999985694885},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C68476402","wikidata":"https://www.wikidata.org/wiki/Q1456936","display_name":"Table of contents","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.28859999775886536},{"id":"https://openalex.org/C36964233","wikidata":"https://www.wikidata.org/wiki/Q7920942","display_name":"Verbal reasoning","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C2767350","wikidata":"https://www.wikidata.org/wiki/Q6662173","display_name":"Business intelligence","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C86827895","wikidata":"https://www.wikidata.org/wiki/Q7098582","display_name":"Opportunistic reasoning","level":4,"score":0.2574000060558319},{"id":"https://openalex.org/C97364631","wikidata":"https://www.wikidata.org/wiki/Q484284","display_name":"Deductive reasoning","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.759","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.759","pdf_url":"https://aclanthology.org/2026.findings-acl.759.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2505.19563","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.19563","pdf_url":"https://arxiv.org/pdf/2505.19563","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:arXiv.org:2505.19563","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2505.19563","pdf_url":"https://arxiv.org/pdf/2505.19563","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2505.19563","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.19563","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.759","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.759","pdf_url":"https://aclanthology.org/2026.findings-acl.759.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414586680.pdf","grobid_xml":"https://content.openalex.org/works/W4414586680.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mathematical":[0],"reasoning":[1,26,47,111,152,155,165],"has":[2,16,33],"long":[3],"been":[4,17,34],"a":[5,97,127],"key":[6,146],"benchmark":[7,128],"for":[8,25,84,182,188,193],"evaluating":[9],"large":[10],"language":[11],"models":[12,183],"(LLMs).Although":[13],"substantial":[14],"progress":[15],"made":[18],"on":[19,70,121],"math":[20,103],"word":[21,104],"problems,":[22],"the":[23,68,80,114],"need":[24],"over":[27,186],"tabular":[28,110],"data":[29],"in":[30,60,88,166],"realworld":[31],"applications":[32,37],"overlooked.For":[35],"instance,":[36],"such":[38],"as":[39],"business":[40],"intelligence":[41],"demand":[42],"not":[43],"only":[44],"multi-step":[45],"numerical":[46],"with":[48,138,176],"tables":[49,73,159,178],"but":[50],"also":[51],"robustness":[52],"to":[53,77,163,184,196],"incomplete":[54],"or":[55],"inconsistent":[56],"information.However,":[57],"comprehensive":[58],"evaluation":[59,115],"this":[61,92,122],"area":[62],"is":[63],"severely":[64],"limited,":[65],"constrained":[66],"by":[67],"reliance":[69],"manually":[71],"collected":[72],"that":[74,100],"are":[75,191],"difficult":[76],"scale":[78],"and":[79,108,119,134,151],"lack":[81],"of":[82,116],"coverage":[83],"potential":[85],"traps":[86],"encountered":[87],"real-world":[89],"scenarios.To":[90],"address":[91],"problem,":[93],"we":[94,124],"propose":[95],"AUTOT2T,":[96],"neuro-symbolic":[98],"framework":[99],"controllably":[101],"transforms":[102],"problems":[105],"into":[106],"scalable":[107],"verified":[109],"tasks,":[112],"enabling":[113],"both":[117],"accuracy":[118],"robustness.Building":[120],"pipeline,":[123],"develop":[125],"TabularMath,":[126],"comprising":[129],"three":[130,145],"progressively":[131],"complex":[132],"subsets":[133],"an":[135],"imperfect":[136],"subset,":[137],"their":[139],"corresponding":[140],"image":[141],"version.Our":[142],"study":[143],"reveals":[144],"observations:":[147],"(1)":[148],"Table":[149],"complexity":[150],"difficulty":[153],"impact":[154],"performance":[156],"jointly;(2)":[157],"Low-quality":[158],"pose":[160],"severe":[161],"risks":[162],"reliable":[164],"current":[167],"LLMs;":[168],"(3)":[169],"Different":[170],"table":[171],"modalities":[172],"show":[173],"similar":[174],"trends,":[175],"textbased":[177],"typically":[179],"being":[180],"easier":[181],"reason":[185],"even":[187],"MLLMs.In-depth":[189],"analyses":[190],"conducted":[192],"each":[194],"observation":[195],"guide":[197],"future":[198],"research.":[199]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-09-29T00:00:00"}