{"id":"https://openalex.org/W7154463862","doi":"https://doi.org/10.48550/arxiv.2604.12843","title":"Growing Pains: Extensible and Efficient LLM Benchmarking Via Fixed Parameter Calibration","display_name":"Growing Pains: Extensible and Efficient LLM Benchmarking Via Fixed Parameter Calibration","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154463862","doi":"https://doi.org/10.48550/arxiv.2604.12843"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12843","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12843","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.2604.12843","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091921041","display_name":"Eliya Habba","orcid":"https://orcid.org/0009-0001-3472-0650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Habba, Eliya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017452853","display_name":"Itay Itzhak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Itzhak, Itay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024163531","display_name":"Asaf Yehudai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yehudai, Asaf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133648182","display_name":"Yotam Perlitz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perlitz, Yotam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133634343","display_name":"Elron Bandel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bandel, Elron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051536706","display_name":"Michal Shmueli-Scheuer","orcid":"https://orcid.org/0000-0002-6386-8726"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shmueli-Scheuer, Michal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133680820","display_name":"Leshem Choshen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choshen, Leshem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133658626","display_name":"Gabriel Stanovsky","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stanovsky, Gabriel","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/T10028","display_name":"Topic Modeling","score":0.48969998955726624,"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/T10028","display_name":"Topic Modeling","score":0.48969998955726624,"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/T10467","display_name":"Psychometric Methodologies and Testing","score":0.0714000016450882,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.04670000076293945,"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":0.824999988079071},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7318000197410583},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6769000291824341},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.642799973487854},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6050000190734863},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5976999998092651},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4503999948501587},{"id":"https://openalex.org/keywords/extensibility","display_name":"Extensibility","score":0.448199987411499}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.824999988079071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7659000158309937},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7318000197410583},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6769000291824341},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.642799973487854},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6050000190734863},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5976999998092651},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5296000242233276},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4503999948501587},{"id":"https://openalex.org/C32833848","wikidata":"https://www.wikidata.org/wiki/Q4115054","display_name":"Extensibility","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C151552104","wikidata":"https://www.wikidata.org/wiki/Q7705809","display_name":"Test suite","level":4,"score":0.43799999356269836},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38580000400543213},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C3018395757","wikidata":"https://www.wikidata.org/wiki/Q1379672","display_name":"Evaluation methods","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2793000042438507},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12843","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12843","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.2604.12843","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12843","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"release":[2],"of":[3,95],"both":[4],"language":[5],"models":[6,22,84],"and":[7,83],"benchmarks":[8,57],"makes":[9],"it":[10,151],"increasingly":[11],"costly":[12],"to":[13,32,54,58,154],"evaluate":[14],"every":[15,18],"model":[16],"on":[17,26,44,88,121],"dataset.":[19,171],"In":[20,118],"practice,":[21],"are":[23,79,85],"often":[24],"evaluated":[25,86],"different":[27,111],"samples,":[28],"making":[29],"scores":[30],"difficult":[31],"compare":[33],"across":[34],"studies.":[35],"To":[36],"address":[37],"this,":[38],"we":[39],"propose":[40],"a":[41,72,98,165],"framework":[42,127],"based":[43],"multidimensional":[45],"Item":[46],"Response":[47],"Theory":[48],"(IRT)":[49],"that":[50,108,150],"uses":[51],"anchor":[52,100,138],"items":[53],"calibrate":[55],"new":[56,170],"the":[59,89,93],"evaluation":[60,74,112,167],"suite":[61],"while":[62,97,160],"holding":[63],"previously":[64],"calibrated":[65],"item":[66],"parameters":[67],"fixed.":[68],"Our":[69],"approach":[70],"supports":[71],"realistic":[73],"setting":[75],"in":[76],"which":[77],"datasets":[78,90],"introduced":[80],"over":[81,158],"time":[82,94,159],"only":[87,136],"available":[91,173],"at":[92,164,174],"evaluation,":[96],"fixed":[99],"set":[101],"for":[102,146],"each":[103],"dataset":[104],"is":[105,152],"used":[106],"so":[107],"results":[109],"from":[110],"periods":[113],"can":[114],"be":[115],"compared":[116],"directly.":[117],"large-scale":[119],"experiments":[120],"more":[122],"than":[123],"$400$":[124],"models,":[125],"our":[126],"predicts":[128],"full-evaluation":[129],"performance":[130],"within":[131],"2-3":[132],"percentage":[133],"points":[134],"using":[135],"$100$":[137],"questions":[139],"per":[140,169],"dataset,":[141],"with":[142],"Spearman":[143],"$\u03c1\\geq":[144],"0.9$":[145],"ranking":[147],"preservation,":[148],"showing":[149],"possible":[153],"extend":[155],"benchmark":[156],"suites":[157],"preserving":[161],"score":[162],"comparability,":[163],"constant":[166],"cost":[168],"Code":[172],"https://github.com/eliyahabba/growing-pains":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}
