{"id":"https://openalex.org/W4416036007","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1118","title":"GRIT: Guided Relational Integration for Efficient Multi-Table Understanding","display_name":"GRIT: Guided Relational Integration for Efficient Multi-Table Understanding","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416036007","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1118"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1118","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1118","pdf_url":"https://aclanthology.org/2025.emnlp-main.1118.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":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.emnlp-main.1118.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062496071","display_name":"Yong Kang","orcid":"https://orcid.org/0000-0003-0991-5314"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yujin Kang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102527987","display_name":"Park Seong Woo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park Seong Woo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072435194","display_name":"Y. Cho","orcid":"https://orcid.org/0000-0002-3418-2506"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoon-Sik Cho","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062496071"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.6863,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.95285199,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"21995","last_page":"22008"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.18629999458789825,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.18629999458789825,"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":0.1598999947309494,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.050999999046325684,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.45730000734329224},{"id":"https://openalex.org/keywords/data-integration","display_name":"Data integration","score":0.3504999876022339},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3093000054359436},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.28870001435279846},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.2761000096797943},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.27559998631477356}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6179999709129333},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38940000534057617},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.emnlp-main.1118","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1118","pdf_url":"https://aclanthology.org/2025.emnlp-main.1118.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":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1118","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1118","pdf_url":"https://aclanthology.org/2025.emnlp-main.1118.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":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G13572568","display_name":null,"funder_award_id":"RS-2021-II211341","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G3034753964","display_name":null,"funder_award_id":"grant","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G342704958","display_name":null,"funder_award_id":"funded","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G6072120315","display_name":null,"funder_award_id":"funded","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321202","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416036007.pdf","grobid_xml":"https://content.openalex.org/works/W4416036007.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"have":[7],"opened":[8],"new":[9],"possibilities":[10],"for":[11,66],"tablebased":[12],"tasks.However,":[13],"most":[14],"existing":[15],"methods":[16],"remain":[17],"confined":[18],"to":[19,25,84],"single-table":[20],"settings,":[21],"limiting":[22],"their":[23],"applicability":[24],"real-world":[26],"databases":[27],"composed":[28],"of":[29],"multiple":[30,67],"interrelated":[31],"tables.In":[32],"multi-table":[33,109],"scenarios,":[34],"LLMs":[35],"face":[36],"two":[37],"key":[38,88],"challenges:":[39],"reasoning":[40],"over":[41],"relational":[42,75],"structures":[43],"beyond":[44],"sequential":[45],"text,":[46],"and":[47,90,99,115],"handling":[48],"the":[49],"input":[50],"length":[51],"limitations":[52],"imposed":[53],"by":[54],"large-scale":[55],"table":[56],"concatenation.To":[57],"address":[58],"these":[59],"issues,":[60],"we":[61],"propose":[62],"Guided":[63],"Relational":[64],"Integration":[65],"Tables":[68],"(GRIT),":[69],"a":[70],"lightweight":[71],"method":[72],"that":[73,93],"converts":[74],"schemas":[76],"into":[77],"LLMfriendly":[78],"textual":[79],"representations.GRIT":[80],"employs":[81],"hashing-based":[82],"techniques":[83],"efficiently":[85],"infer":[86],"primary-foreign":[87],"relationships":[89],"constructs":[91],"prompts":[92],"explicitly":[94],"encode":[95],"relevant":[96],"join":[97],"paths":[98],"question-relevant":[100],"columns.GRIT":[101],"consistently":[102],"improves":[103],"table-column":[104],"retrieval":[105],"performance":[106],"across":[107],"diverse":[108],"benchmarks":[110],"while":[111],"significantly":[112],"reducing":[113],"memory":[114],"computational":[116],"overhead.":[117]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-11-08T00:00:00"}
