{"id":"https://openalex.org/W7140749555","doi":"https://doi.org/10.48550/arxiv.2603.23506","title":"Leveraging Computerized Adaptive Testing for Cost-effective Evaluation of Large Language Models in Medical Benchmarking","display_name":"Leveraging Computerized Adaptive Testing for Cost-effective Evaluation of Large Language Models in Medical Benchmarking","publication_year":2026,"publication_date":"2026-02-28","ids":{"openalex":"https://openalex.org/W7140749555","doi":"https://doi.org/10.48550/arxiv.2603.23506"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23506","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.23506","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130698047","display_name":"Tianpeng Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Tianpeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130658443","display_name":"Zhehan Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Zhehan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130706147","display_name":"Jiayi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiayi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123904378","display_name":"Shicong Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Shicong","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/T10467","display_name":"Psychometric Methodologies and Testing","score":0.9083999991416931,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.9083999991416931,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.01759999990463257,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.010900000110268593,"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/benchmarking","display_name":"Benchmarking","score":0.8634999990463257},{"id":"https://openalex.org/keywords/computerized-adaptive-testing","display_name":"Computerized adaptive testing","score":0.7824000120162964},{"id":"https://openalex.org/keywords/item-response-theory","display_name":"Item response theory","score":0.636900007724762},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6340000033378601},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.54830002784729},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.3635999858379364},{"id":"https://openalex.org/keywords/readability","display_name":"Readability","score":0.35420000553131104},{"id":"https://openalex.org/keywords/data-validation","display_name":"Data validation","score":0.3082999885082245}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8634999990463257},{"id":"https://openalex.org/C144352353","wikidata":"https://www.wikidata.org/wiki/Q2920411","display_name":"Computerized adaptive testing","level":3,"score":0.7824000120162964},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7587000131607056},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.636900007724762},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6340000033378601},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4081000089645386},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4016999900341034},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3917999863624573},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C2778143727","wikidata":"https://www.wikidata.org/wiki/Q1820650","display_name":"Readability","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C92446256","wikidata":"https://www.wikidata.org/wiki/Q3306762","display_name":"Data validation","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C156639949","wikidata":"https://www.wikidata.org/wiki/Q13362737","display_name":"Item bank","level":4,"score":0.29440000653266907},{"id":"https://openalex.org/C74143277","wikidata":"https://www.wikidata.org/wiki/Q7705812","display_name":"Test theory","level":3,"score":0.2919999957084656},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C106436119","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assurance","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23506","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.23506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23506","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,67,193],"rapid":[1],"proliferation":[2],"of":[3,61,86,149,187],"large":[4],"language":[5],"models":[6],"(LLMs)":[7],"in":[8,53,65,166,191],"healthcare":[9],"creates":[10],"an":[11,83,104],"urgent":[12],"need":[13],"for":[14,38,58,183,212],"scalable":[15],"and":[16,33,44,82,103,116,169,203,207],"psychometrically":[17],"sound":[18],"evaluation":[19,85],"methods.":[20],"Conventional":[21],"static":[22],"benchmarks":[23],"are":[24],"costly":[25],"to":[26,30,77,159],"administer":[27],"repeatedly,":[28],"vulnerable":[29],"data":[31],"contamination,":[32],"lack":[34],"calibrated":[35],"measurement":[36],"properties":[37],"fine-grained":[39],"performance":[40,175],"tracking.":[41],"We":[42],"propose":[43],"validate":[45],"a":[46,70,73,90,120,135,180,200,210],"computerized":[47],"adaptive":[48,105,195],"testing":[49],"(CAT)":[50],"framework":[51,182],"grounded":[52],"item":[54,93,101],"response":[55],"theory":[56],"(IRT)":[57],"efficient":[59],"assessment":[60],"standardized":[62,201],"medical":[63,92,189],"knowledge":[64,190],"LLMs.":[66,192],"study":[68],"comprises":[69],"two-phase":[71],"design:":[72],"Monte":[74],"Carlo":[75],"simulation":[76],"identify":[78],"optimal":[79],"CAT":[80],"configurations":[81],"empirical":[84],"38":[87],"LLMs":[88],"using":[89,145],"human-calibrated":[91],"bank.":[94],"Each":[95],"model":[96],"completed":[97],"both":[98],"the":[99,150],"full":[100],"bank":[102],"test":[106],"that":[107,130],"dynamically":[108],"selected":[109],"items":[110],"based":[111],"on":[112],"real-time":[113],"ability":[114],"estimates":[115,133,140],"terminated":[117],"upon":[118],"reaching":[119],"predefined":[121],"reliability":[122],"threshold":[123],"(standard":[124],"error":[125],"&lt;=":[126],"0.3).":[127],"Results":[128],"show":[129],"CAT-derived":[131],"proficiency":[132],"achieved":[134],"near-perfect":[136],"correlation":[137],"with":[138,163],"full-bank":[139],"(r":[141],"=":[142],"0.988)":[143],"while":[144,172],"only":[146],"1.3":[147],"percent":[148],"items.":[151],"Evaluation":[152],"time":[153],"was":[154],"reduced":[155],"from":[156],"several":[157],"hours":[158],"minutes":[160],"per":[161],"model,":[162],"substantial":[164],"reductions":[165],"token":[167],"usage":[168],"computational":[170],"cost,":[171],"preserving":[173],"inter-model":[174],"rankings.":[176],"This":[177],"work":[178],"establishes":[179],"psychometric":[181],"rapid,":[184],"low-cost":[185],"benchmarking":[186],"foundational":[188],"proposed":[194],"methodology":[196],"is":[197,208],"intended":[198],"as":[199],"pre-screening":[202],"continuous":[204],"monitoring":[205],"tool":[206],"not":[209],"substitute":[211],"real-world":[213],"clinical":[214],"validation":[215],"or":[216],"safety-oriented":[217],"prospective":[218],"studies.":[219]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-27T00:00:00"}
