{"id":"https://openalex.org/W4415327343","doi":"https://doi.org/10.18653/v1/2026.acl-long.149","title":"CRAFT: Training-Free Cascaded Retrieval for Tabular QA","display_name":"CRAFT: Training-Free Cascaded Retrieval for Tabular QA","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4415327343","doi":"https://doi.org/10.18653/v1/2026.acl-long.149"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.acl-long.149","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.149","pdf_url":"https://aclanthology.org/2026.acl-long.149.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","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.acl-long.149.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002743442","display_name":"Adarsh Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adarsh Singh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106403482","display_name":"Kushal Raj Bhandari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kushal Raj Bhandari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031928873","display_name":"Jianxi Gao","orcid":"https://orcid.org/0000-0002-3952-208X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianxi Gao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047523675","display_name":"Soham Dan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soham Dan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100667613","display_name":"Vivek Gupta","orcid":"https://orcid.org/0000-0002-7830-4616"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vivek Gupta","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":0.01663762,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3284","last_page":"3298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9750000238418579,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9750000238418579,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9693999886512756,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10028","display_name":"Topic Modeling","score":0.9628000259399414,"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/question-answering","display_name":"Question answering","score":0.7093999981880188},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.6363999843597412},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5799000263214111},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5285999774932861},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.43880000710487366},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.43630000948905945},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.43540000915527344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.808899998664856},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7093999981880188},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.6363999843597412},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5799000263214111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.535099983215332},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5285999774932861},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4862000048160553},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4339999854564667},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.40549999475479126},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3411000072956085},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31060001254081726},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C174252522","wikidata":"https://www.wikidata.org/wiki/Q3816772","display_name":"Natural language user interface","level":3,"score":0.25769999623298645}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2026.acl-long.149","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.149","pdf_url":"https://aclanthology.org/2026.acl-long.149.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2505.14984","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2505.14984","pdf_url":"https://arxiv.org/pdf/2505.14984","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.2505.14984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.14984","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.acl-long.149","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.149","pdf_url":"https://aclanthology.org/2026.acl-long.149.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1658287642","display_name":"CAREER: Network Resilience: Theories, Algorithms, and Applications","funder_award_id":"2047488","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309500","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415327343.pdf","grobid_xml":"https://content.openalex.org/works/W4415327343.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Open-Domain":[0],"Table":[1],"Question":[2],"Answering":[3],"(TQA)":[4],"involves":[5],"retrieving":[6],"relevant":[7],"tables":[8,76],"from":[9],"a":[10,58,66,72,158],"large":[11],"corpus":[12],"to":[13,48,70],"answer":[14],"natural":[15],"language":[16],"queries.Traditional":[17],"dense":[18,82],"retrieval":[19,34,61,68,87,175],"models,":[20],"such":[21],"as":[22,84],"DTR":[23],"and":[24,51,96,109,118,153,160,172],"DPR,":[25],"not":[26],"only":[27],"incur":[28],"high":[29],"computational":[30],"costs":[31],"for":[32,163],"large-scale":[33],"tasks":[35],"but":[36],"also":[37,125],"require":[38],"retraining":[39],"or":[40],"finetuning":[41],"on":[42,121,130],"new":[43],"datasets,":[44],"limiting":[45],"their":[46],"adaptability":[47],"evolving":[49],"domains":[50],"knowledge.In":[52],"this":[53],"work,":[54],"we":[55,89],"propose":[56],"CRAFT,":[57],"zeroshot,":[59],"cascaded":[60],"approach":[62],"that":[63],"first":[64],"uses":[65],"sparse":[67],"model":[69],"filter":[71],"subset":[73],"of":[74],"candidate":[75],"before":[77],"applying":[78],"more":[79,132],"computationally":[80],"expensive":[81],"models":[83],"rerankers.To":[85],"improve":[86],"quality,":[88],"enrich":[90],"table":[91,164],"representations":[92],"with":[93],"descriptive":[94],"titles":[95],"summaries":[97],"generated":[98],"by":[99],"Gemini":[100],"Flash":[101],"1.5,":[102],"enabling":[103],"richer":[104],"semantic":[105],"matching":[106],"between":[107,169],"queries":[108],"tabular":[110],"structures.Our":[111],"method":[112],"outperforms":[113],"state-of-the-art":[114],"(SOTA)":[115],"sparse,":[116],"dense,":[117],"hybrid":[119],"retrievers":[120],"the":[122,131,144,167],"NQ-Tables":[123],"dataset.It":[124],"demonstrates":[126],"strong":[127],"zero-shot":[128],"performance":[129],"challenging":[133],"OTT-QA":[134],"benchmark,":[135],"achieving":[136],"competitive":[137],"results":[138],"at":[139],"higher":[140],"recall":[141],"thresholds,":[142],"where":[143],"task":[145],"requires":[146],"multi-hop":[147],"reasoning":[148],"across":[149],"both":[150],"textual":[151],"passages":[152],"relational":[154],"tables.This":[155],"work":[156],"establishes":[157],"scalable":[159],"adaptable":[161],"paradigm":[162],"retrieval,":[165],"bridging":[166],"gap":[168],"fine-tuned":[170],"architectures":[171],"lightweight,":[173],"plug-and-play":[174],"systems.":[176]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-19T00:00:00"}