{"id":"https://openalex.org/W4412073491","doi":"https://doi.org/10.1145/3748239.3748248","title":"Advancing Table Understanding of Large Language Models via Feature Re-ordering","display_name":"Advancing Table Understanding of Large Language Models via Feature Re-ordering","publication_year":2025,"publication_date":"2025-07-07","ids":{"openalex":"https://openalex.org/W4412073491","doi":"https://doi.org/10.1145/3748239.3748248"},"language":"en","primary_location":{"id":"doi:10.1145/3748239.3748248","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3748239.3748248","pdf_url":null,"source":{"id":"https://openalex.org/S4210176598","display_name":"ACM SIGKDD Explorations Newsletter","issn_l":"1931-0145","issn":["1931-0145","1931-0153"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGKDD Explorations Newsletter","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080590912","display_name":"Guanchu Wang","orcid":"https://orcid.org/0000-0003-3258-762X"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guanchu Wang","raw_affiliation_strings":["Rice University"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072866256","display_name":"Yuzhong Chen","orcid":"https://orcid.org/0009-0001-2578-3772"},"institutions":[{"id":"https://openalex.org/I164956901","display_name":"Visa (United Kingdom)","ror":"https://ror.org/05syhdw25","country_code":"GB","type":"company","lineage":["https://openalex.org/I164956901","https://openalex.org/I4210148469"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yuzhong Chen","raw_affiliation_strings":["Visa Research"],"affiliations":[{"raw_affiliation_string":"Visa Research","institution_ids":["https://openalex.org/I164956901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004341287","display_name":"Huiyuan Chen","orcid":"https://orcid.org/0000-0002-6360-558X"},"institutions":[{"id":"https://openalex.org/I164956901","display_name":"Visa (United Kingdom)","ror":"https://ror.org/05syhdw25","country_code":"GB","type":"company","lineage":["https://openalex.org/I164956901","https://openalex.org/I4210148469"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Huiyuan Chen","raw_affiliation_strings":["Visa Research"],"affiliations":[{"raw_affiliation_string":"Visa Research","institution_ids":["https://openalex.org/I164956901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032658855","display_name":"Xiaoxuan Fan","orcid":"https://orcid.org/0000-0003-0501-771X"},"institutions":[{"id":"https://openalex.org/I164956901","display_name":"Visa (United Kingdom)","ror":"https://ror.org/05syhdw25","country_code":"GB","type":"company","lineage":["https://openalex.org/I164956901","https://openalex.org/I4210148469"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiran Fan","raw_affiliation_strings":["Visa Research"],"affiliations":[{"raw_affiliation_string":"Visa Research","institution_ids":["https://openalex.org/I164956901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100669433","display_name":"Junpeng Wang","orcid":"https://orcid.org/0000-0002-1130-9914"},"institutions":[{"id":"https://openalex.org/I164956901","display_name":"Visa (United Kingdom)","ror":"https://ror.org/05syhdw25","country_code":"GB","type":"company","lineage":["https://openalex.org/I164956901","https://openalex.org/I4210148469"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Junpeng Wang","raw_affiliation_strings":["Visa Research"],"affiliations":[{"raw_affiliation_string":"Visa Research","institution_ids":["https://openalex.org/I164956901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050798878","display_name":"Xiaoting Li","orcid":"https://orcid.org/0000-0002-2600-0223"},"institutions":[{"id":"https://openalex.org/I164956901","display_name":"Visa (United Kingdom)","ror":"https://ror.org/05syhdw25","country_code":"GB","type":"company","lineage":["https://openalex.org/I164956901","https://openalex.org/I4210148469"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiaoting Li","raw_affiliation_strings":["Visa Research"],"affiliations":[{"raw_affiliation_string":"Visa Research","institution_ids":["https://openalex.org/I164956901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103162777","display_name":"Mingzhi Hu","orcid":"https://orcid.org/0009-0005-5694-6780"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingzhi Hu","raw_affiliation_strings":["Worcester Polytechnic Institute"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101857873","display_name":"Chia-Yuan Chang","orcid":"https://orcid.org/0009-0001-1889-612X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chia-Yuan Chang","raw_affiliation_strings":["Texas A&amp;M University"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068477431","display_name":"Xia Hu","orcid":"https://orcid.org/0000-0003-2234-3226"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xia Hu","raw_affiliation_strings":["Rice University"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5080590912"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08795354,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":"1","first_page":"112","last_page":"123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9901999831199646,"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":0.9901999831199646,"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":0.9469000101089478,"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":0.9409000277519226,"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/feature","display_name":"Feature (linguistics)","score":0.6563388705253601},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.5943909883499146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5142068862915039},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38000327348709106},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.3355260491371155},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.13246279954910278},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.0858483612537384}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6563388705253601},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.5943909883499146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5142068862915039},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38000327348709106},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3355260491371155},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.13246279954910278},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0858483612537384}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748239.3748248","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3748239.3748248","pdf_url":null,"source":{"id":"https://openalex.org/S4210176598","display_name":"ACM SIGKDD Explorations Newsletter","issn_l":"1931-0145","issn":["1931-0145","1931-0153"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGKDD Explorations Newsletter","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W3120740533","https://openalex.org/W4221143046","https://openalex.org/W4307001389","https://openalex.org/W4367365458","https://openalex.org/W4377130677","https://openalex.org/W4378474365","https://openalex.org/W4392366650","https://openalex.org/W4400529822","https://openalex.org/W6788247690"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4394360958"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"exhibit":[4],"exceptional":[5],"proficiency":[6],"in":[7,71],"comprehending":[8],"human":[9],"language.":[10],"Despite":[11],"their":[12],"significant":[13,97,231],"success":[14],"across":[15,195],"a":[16,25,96,104,149],"wide":[17],"array":[18],"of":[19,35,55,60,83,130,176],"tasks,":[20],"understanding":[21],"tabular":[22,29,89,131,227],"data":[23,30,132,141,170],"remains":[24],"challenging":[26],"task.":[27,153],"Especially,":[28],"lacks":[31],"an":[32,48,164],"intrinsic":[33],"order":[34,50,77,93,129],"the":[36,53,58,81,92,121,127,140,145,177,182,219],"different":[37,196],"features":[38,194,214],"(table":[39],"fields),":[40],"whereas":[41],"LLMs":[42,61,84,204],"take":[43],"only":[44],"sequential":[45],"inputs.":[46],"Consequently,":[47],"artificial":[49],"is":[51,159,189],"imposed,":[52],"impact":[54],"which":[56],"on":[57,85,200],"performance":[59,82,185,188,211],"has":[62],"not":[63],"yet":[64],"been":[65],"thoroughly":[66],"investigated.":[67],"Surprisingly,":[68],"as":[69,148],"discovered":[70],"this":[72,74],"work,":[73],"artificially":[75],"induced":[76],"bias":[78,94],"dramatically":[79],"influences":[80],"tasks":[86],"related":[87],"to":[88,115,137,161,207],"data.":[90],"Mitigating":[91],"presents":[95],"challenge.":[98],"To":[99],"address":[100],"this,":[101],"we":[102],"propose":[103],"simple":[105],"and":[106,133,203],"cost-effective":[107],"method,":[108],"Re-Ordering":[109],"Tabular":[110],"feATures":[111],"fOR":[112],"LLM":[113,228],"(ROTATOR-LLM),":[114],"conduct":[116],"test-time":[117],"compute":[118],"without":[119],"fine-tuning":[120],"base":[122],"LLM.":[123],"Aiming":[124],"at":[125],"optimizing":[126],"feature":[128,150,166,179],"boosting":[134],"LLMs'":[135],"capability":[136],"better":[138],"understand":[139],"semantics,":[142],"ROTATOR-LLM":[143,217],"re-frames":[144],"ordering":[146],"problem":[147],"trajectory":[151,167],"generation":[152],"A":[154],"dynamic":[155],"programming":[156],"based":[157],"meta-controller":[158],"trained":[160],"auto-regressively":[162],"generate":[163],"individualized":[165],"for":[168],"each":[169],"instance":[171],"via":[172,213],"accumulative":[173],"value":[174],"estimation":[175],"serialized":[178],"input":[180],"through":[181],"LLM's":[183],"final":[184],"metrics.":[186],"Model":[187],"maximized":[190],"by":[191,216],"iteratively":[192],"selecting":[193],"steps.":[197],"Experimental":[198],"results":[199],"multiple":[201],"datasets":[202],"show":[205],"close":[206],"or":[208],"over":[209],"20%":[210],"boosts":[212],"reordered":[215],"against":[218],"un-ordered":[220],"counterpart.":[221],"Meanwhile,":[222],"it":[223],"outperforms":[224],"stateof-":[225],"the-Art":[226],"methods":[229],"with":[230],"margin.":[232]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
