{"id":"https://openalex.org/W7155499609","doi":"https://doi.org/10.48550/arxiv.2604.21549","title":"Unbiased Prevalence Estimation with Multicalibrated LLMs","display_name":"Unbiased Prevalence Estimation with Multicalibrated LLMs","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155499609","doi":"https://doi.org/10.48550/arxiv.2604.21549"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.21549","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21549","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.21549","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120655353","display_name":"Fridolin Linder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Linder, Fridolin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134540113","display_name":"Thomas Leeper","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leeper, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134515187","display_name":"Daniel Haimovich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haimovich, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134559342","display_name":"Niek Tax","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tax, Niek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134474071","display_name":"Lorenzo Perini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perini, Lorenzo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134484637","display_name":"Milan Vojnovic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vojnovic, Milan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.10779999941587448,"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":0.10779999941587448,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.07209999859333038,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.06019999831914902,"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/estimator","display_name":"Estimator","score":0.6811000108718872},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6500999927520752},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6137999892234802},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5407000184059143},{"id":"https://openalex.org/keywords/standard-error","display_name":"Standard error","score":0.4927000105381012},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4406000077724457},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4341000020503998},{"id":"https://openalex.org/keywords/point-estimation","display_name":"Point estimation","score":0.4307999908924103},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.4278999865055084}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6811000108718872},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6567999720573425},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6500999927520752},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6137999892234802},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6014000177383423},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5407000184059143},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.4278999865055084},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.37529999017715454},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3702000081539154},{"id":"https://openalex.org/C2909318450","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Unbiased Estimation","level":3,"score":0.35359999537467957},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31790000200271606},{"id":"https://openalex.org/C6571938","wikidata":"https://www.wikidata.org/wiki/Q3274486","display_name":"Omitted-variable bias","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C88629717","wikidata":"https://www.wikidata.org/wiki/Q17056323","display_name":"Information bias","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C129963666","wikidata":"https://www.wikidata.org/wiki/Q17105857","display_name":"Small area estimation","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2754000127315521},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.21549","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21549","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.21549","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21549","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.5649010539054871,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Estimating":[0],"the":[1,63,130,154,184],"prevalence":[2,75,149],"of":[3],"a":[4,7,99,121],"category":[5],"in":[6,175],"population":[8],"using":[9,153,165],"imperfect":[10],"measurement":[11,101],"devices":[12],"(diagnostic":[13],"tests,":[14],"classifiers,":[15],"or":[16],"large":[17],"language":[18],"models)":[19],"is":[20,71],"fundamental":[21],"to":[22,86,98,139],"science,":[23],"public":[24],"health,":[25],"and":[26,29,55,82,158],"online":[27],"trust":[28],"safety.":[30],"Standard":[31,80],"approaches":[32],"correct":[33],"for":[34,73],"known":[35],"device":[36],"error":[37],"rates":[38,42],"but":[39],"assume":[40],"these":[41],"remain":[43],"stable":[44],"across":[45,150,162],"populations.":[46],"We":[47],"show":[48],"this":[49,88],"assumption":[50],"fails":[51],"under":[52,77],"covariate":[53],"shift":[54,118],"that":[56,111,170,179],"multicalibration,":[57],"which":[58,189],"enforces":[59],"calibration":[60,81,180],"conditional":[61],"on":[62,69,96,133],"input":[64],"features":[65],"rather":[66],"than":[67],"just":[68],"average,":[70],"sufficient":[72],"unbiased":[74],"estimation":[76],"such":[78],"shift.":[79],"quantification":[83],"methods":[84,113],"fail":[85],"provide":[87],"guarantee.":[89],"Our":[90],"work":[91,95],"connects":[92],"recent":[93],"theoretical":[94,136],"fairness":[97],"longstanding":[100],"problem":[102],"spanning":[103],"nearly":[104],"all":[105],"academic":[106],"disciplines.":[107],"A":[108],"simulation":[109],"confirms":[110],"standard":[112],"exhibit":[114],"bias":[115,174],"growing":[116],"with":[117],"magnitude,":[119],"while":[120,177],"multicalibrated":[122],"estimator":[123],"maintains":[124],"near-zero":[125],"bias.":[126],"While":[127],"we":[128],"focus":[129],"discussion":[131],"mostly":[132],"LLMs,":[134],"our":[135],"results":[137],"apply":[138],"any":[140],"classification":[141],"model.":[142],"Two":[143],"empirical":[144],"applications":[145],"--":[146,168],"estimating":[147],"employment":[148],"U.S.":[151],"states":[152],"American":[155],"Community":[156],"Survey,":[157],"classifying":[159],"political":[160],"texts":[161],"four":[163],"countries":[164],"an":[166],"LLM":[167],"demonstrate":[169],"multicalibration":[171],"substantially":[172],"reduces":[173],"practice,":[176],"highlighting":[178],"data":[181],"should":[182],"cover":[183],"key":[185],"feature":[186],"dimensions":[187],"along":[188],"target":[190],"populations":[191],"may":[192],"differ.":[193]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-25T00:00:00"}