{"id":"https://openalex.org/W7163014233","doi":"https://doi.org/10.48550/arxiv.2605.30615","title":"Improving Selective Classification with Pairwise Queries for Binary Classification","display_name":"Improving Selective Classification with Pairwise Queries for Binary Classification","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7163014233","doi":"https://doi.org/10.48550/arxiv.2605.30615"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30615","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30615","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.30615","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137571107","display_name":"Harsh Vardhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vardhan, Harsh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064726544","display_name":"Sunav Choudhary","orcid":"https://orcid.org/0000-0002-7711-487X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choudhary, Sunav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016869559","display_name":"Natwar Modani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Modani, Natwar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5051046818","display_name":"Arya Mazumdar","orcid":"https://orcid.org/0000-0003-4605-7996"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mazumdar, Arya","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":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/T12072","display_name":"Machine Learning and Algorithms","score":0.32749998569488525,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.32749998569488525,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.20630000531673431,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.1289999932050705,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.9235000014305115},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.607699990272522},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47510001063346863},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4553999900817871},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.362199991941452},{"id":"https://openalex.org/keywords/binary-data","display_name":"Binary data","score":0.29820001125335693}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.9235000014305115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6305999755859375},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.607699990272522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5213000178337097},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4553999900817871},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45019999146461487},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3400999903678894},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.29820001125335693},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25940001010894775},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30615","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30615","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30615","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30615","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,161],"selective":[1,115],"classification,":[2],"a":[3,61,132,174],"model":[4,50],"predicts":[5],"the":[6,43,49,58,68,101,120,128,181,187],"labels":[7,19],"of":[8,60],"data":[9],"samples":[10,21,30,110],"where":[11],"it":[12,24],"is":[13,25,38,45],"confident,":[14],"and":[15,111,153],"abstains":[16],"from":[17],"predicting":[18],"for":[20,42,150,185],"on":[22,54,77,122],"which":[23,37,71,131],"not":[26],"confident.":[27],"The":[28,40],"rejected":[29],"are":[31],"often":[32],"labeled":[33],"by":[34,89],"an":[35,139],"expert,":[36],"expensive.":[39],"budget":[41],"expert":[44],"best":[46],"utilized":[47],"when":[48],"has":[51],"low":[52],"error":[53,76,121],"non-rejected":[55,78,123],"samples.":[56,124],"However,":[57],"estimate":[59],"model's":[62,69],"confidence":[63,141,183],"might":[64],"be":[65,112],"inconsistent":[66,140],"with":[67],"predictions,":[70],"can":[72,82,107],"lead":[73],"to":[74,100,118],"high":[75],"points.":[79],"Such":[80],"situations":[81],"readily":[83],"occur":[84],"in":[85],"in-context":[86,155],"binary":[87,158],"classification":[88,116,159],"LLMs.":[90],"To":[91],"remedy":[92],"this,":[93],"we":[94,126,165],"propose":[95],"making":[96],"additional":[97],"pairwise":[98,105,136,171],"queries":[99,106,137],"same":[102],"model.":[103],"These":[104],"detect":[108],"high-error":[109],"incorporated":[113],"into":[114],"techniques":[117],"reduce":[119],"Theoretically,":[125],"establish":[127],"conditions":[129],"under":[130],"simple":[133],"algorithm":[134],"using":[135,170,179],"outperforms":[138],"estimate.":[142],"We":[143],"support":[144],"this":[145],"insight":[146],"through":[147],"extensive":[148],"experiments":[149],"$1$":[151],"synthetic":[152],"$4$":[154],"learning-based":[156],"real":[157],"datasets.":[160],"all":[162],"these":[163],"cases,":[164],"show":[166],"that":[167],"our":[168],"algorithms,":[169],"queries,":[172],"obtain":[173],"better":[175],"accuracy-cost":[176],"tradeoff":[177],"than":[178],"only":[180],"raw":[182],"estimates,":[184],"instance,":[186],"LLM's":[188],"next-token":[189],"logits.":[190]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-02T00:00:00"}