{"id":"https://openalex.org/W7155501227","doi":"https://doi.org/10.1109/icsc67292.2026.00038","title":"Can Large Language Models Rank Tabular Data Fairly?","display_name":"Can Large Language Models Rank Tabular Data Fairly?","publication_year":2026,"publication_date":"2026-02-02","ids":{"openalex":"https://openalex.org/W7155501227","doi":"https://doi.org/10.1109/icsc67292.2026.00038"},"language":null,"primary_location":{"id":"doi:10.1109/icsc67292.2026.00038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsc67292.2026.00038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Semantic Computing (ICSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5034293010","display_name":"Oluseun Olulana","orcid":null},"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":"Oluseun Olulana","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041530401","display_name":"Kathleen Cachel","orcid":"https://orcid.org/0000-0002-1260-0914"},"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":"Kathleen Cachel","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021734616","display_name":"Fabr\u00edcio Murai","orcid":"https://orcid.org/0000-0003-4487-6381"},"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":"Fabricio Murai","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134535186","display_name":"Elke A. Rundensteiner","orcid":null},"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":"Elke Rundensteiner","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Data Science,Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107077323"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42607393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"226","last_page":"229"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.1543000042438507,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.1543000042438507,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.08919999748468399,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.06199999898672104,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.47049999237060547},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.28700000047683716},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.28279998898506165},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.2711000144481659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.558899998664856},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4065000116825104},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3100999891757965},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2791999876499176},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2700999975204468},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsc67292.2026.00038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsc67292.2026.00038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Semantic Computing (ICSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6332636475563049,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2069870183","https://openalex.org/W2108862644","https://openalex.org/W2787991113","https://openalex.org/W2950173087","https://openalex.org/W3012903288","https://openalex.org/W3205761523","https://openalex.org/W4280652991","https://openalex.org/W4386302153","https://openalex.org/W4386728933","https://openalex.org/W4391622928","https://openalex.org/W4404518169","https://openalex.org/W4404518243","https://openalex.org/W4404518586","https://openalex.org/W4408855668"],"related_works":[],"abstract_inverted_index":{"As":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"are":[5,200],"increasingly":[6],"applied":[7],"to":[8,14,47,67,85,96,125,146,175,187],"critical":[9],"tasks,":[10],"it":[11],"is":[12,70,77],"imperative":[13],"understand":[15],"both":[16],"the":[17,20,58,65,90,144],"promise":[18],"and":[19,157,197],"risks":[21],"of":[22,39],"LLM":[23,51,81,136],"use":[24],"cases":[25],"that":[26],"can":[27,170],"directly":[28],"impact":[29],"humans,":[30],"such":[31,73],"as":[32],"ranking":[33,49,182],"and,":[34],"in":[35],"particular,":[36],"fair":[37],"re-ranking":[38],"applicants":[40],"for":[41,80,135],"jobs":[42],"or":[43],"college":[44],"admissions.":[45],"Similar":[46],"traditional":[48,176],"solutions,":[50],"outputs":[52],"risk":[53],"reflecting":[54],"societal":[55],"biases":[56],"from":[57],"training":[59],"data.":[60],"In":[61],"many":[62],"social":[63],"applications,":[64],"data":[66,100],"be":[68,94],"ranked":[69],"tabular,":[71],"yet":[72],"an":[74],"input":[75,132],"format":[76],"not":[78],"optimized":[79],"consumption":[82],"(in":[83],"contrast":[84],"textual":[86],"documents).":[87],"This":[88],"raises":[89],"question:":[91],"Can":[92],"LLMs":[93,169],"designed":[95],"fairly":[97],"re-rank":[98],"tabular":[99,127],"while":[101,180],"preserving":[102],"utility?":[103],"To":[104],"address":[105],"this":[106],"open":[107],"question,":[108],"we":[109],"design":[110],"a":[111,119,130,149],"new":[112],"evaluation":[113],"methodology,":[114],"titled":[115],"Fairer-TabLLM.":[116],"FAIRER-TABLLM":[117],"incorporates":[118],"simple":[120],"but":[121],"effective":[122],"serialization":[123],"strategy":[124,141],"transform":[126],"rankings":[128],"into":[129],"structured":[131],"representation":[133,159],"ready":[134],"consumption.":[137],"Our":[138,161],"modular":[139],"prompting":[140],"gives":[142],"users":[143],"option":[145],"explicitly":[147],"state":[148],"fairness":[150,156,172,193],"objective,":[151],"choosing":[152],"between":[153],"(1)":[154],"exposure":[155],"(2)":[158],"fairness.":[160],"results":[162],"show":[163],"that,":[164],"under":[165],"carefully":[166],"specified":[167],"prompts,":[168],"achieve":[171],"levels":[173],"comparable":[174],"algorithms":[177],"(e.g.,":[178],"DetConstSort)":[179],"maintaining":[181],"utility.":[183],"Additionally,":[184],"incorporating":[185],"shots":[186],"leverage":[188],"in-context":[189],"learning":[190],"consistently":[191],"improves":[192],"outcomes.":[194],"Source":[195],"code":[196],"experimental":[198],"artifacts":[199],"available":[201],"here:":[202],"https://github.com/sewen007/FaiReRTabLLM":[203]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-25T00:00:00"}
