{"id":"https://openalex.org/W4406202452","doi":"https://doi.org/10.32388/f319w4","title":"SMOOTHIE: Label Free Language Model Routing","display_name":"SMOOTHIE: Label Free Language Model Routing","publication_year":2025,"publication_date":"2025-01-09","ids":{"openalex":"https://openalex.org/W4406202452","doi":"https://doi.org/10.32388/f319w4"},"language":"en","primary_location":{"id":"doi:10.32388/f319w4","doi":"https://doi.org/10.32388/f319w4","is_oa":true,"landing_page_url":"https://doi.org/10.32388/f319w4","pdf_url":"https://www.qeios.com/read/F319W4/pdf","source":{"id":"https://openalex.org/S4210177074","display_name":"Qeios","issn_l":"2632-3834","issn":["2632-3834"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["anvur-scientific","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Qeios","raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.qeios.com/read/F319W4/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068178240","display_name":"Neel Guha","orcid":"https://orcid.org/0009-0003-5120-1726","observed_orcids":["https://orcid.org/0009-0003-5120-1726"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neel Guha","raw_affiliation_strings":["Stanford University"],"raw_orcid":"https://orcid.org/0009-0003-5120-1726","affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048424298","display_name":"Mayee F. Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mayee F. Chen","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101230903","display_name":"Trevor Chow","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Trevor Chow","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115057200","display_name":"Ishan S. Khare","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ishan S. Khare","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103852640","display_name":"Christopher R\u00e9","orcid":"https://orcid.org/0000-0002-4109-0108","observed_orcids":["https://orcid.org/0000-0002-4109-0108"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher R\u00e9","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.9150000214576721,"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":0.9150000214576721,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.05130000039935112,"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":0.004100000020116568,"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/weakly-supervised-learning","display_name":"weakly supervised learning","score":0.8669999837875366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6168271899223328},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.487032949924469},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4224034249782562},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32981953024864197},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.20933499932289124}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32388/f319w4","doi":"https://doi.org/10.32388/f319w4","is_oa":true,"landing_page_url":"https://doi.org/10.32388/f319w4","pdf_url":"https://www.qeios.com/read/F319W4/pdf","source":{"id":"https://openalex.org/S4210177074","display_name":"Qeios","issn_l":"2632-3834","issn":["2632-3834"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["anvur-scientific","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Qeios","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.32388/f319w4","doi":"https://doi.org/10.32388/f319w4","is_oa":true,"landing_page_url":"https://doi.org/10.32388/f319w4","pdf_url":"https://www.qeios.com/read/F319W4/pdf","source":{"id":"https://openalex.org/S4210177074","display_name":"Qeios","issn_l":"2632-3834","issn":["2632-3834"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["anvur-scientific","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Qeios","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1447151805","display_name":"AF: Medium: Collaborative Research: Beyond Sparsity: Refined Measures of Complexity for Linear Algebra","funder_award_id":"1763315","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2040692823","display_name":"SHF: Medium: Collaborative Research: From Volume to Velocity: Big Data Analytics in Near-Realtime","funder_award_id":"1563078","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3804407261","display_name":"Collaborative Research: Hardware-Aware Matrix Computations for Deep Learning Applications","funder_award_id":"2247015","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7568964499","display_name":"RTML: Large: Continuous Adaptation for Decision Streams","funder_award_id":"1937301","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G789143534","display_name":"Mobility Data Integration to Insight","funder_award_id":"U54EB020405","funder_id":"https://openalex.org/F4320337363","funder_display_name":"National Institute of Biomedical Imaging and Bioengineering"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307894","display_name":"Accenture","ror":"https://ror.org/013g16z83"},{"id":"https://openalex.org/F4320316785","display_name":"VMware","ror":"https://ror.org/01mwmh458"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320337363","display_name":"National Institute of Biomedical Imaging and Bioengineering","ror":"https://ror.org/00372qc85"},{"id":"https://openalex.org/F4320337807","display_name":"U.S. Army Combat Capabilities Development Command","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406202452.pdf","grobid_xml":"https://content.openalex.org/works/W4406202452.grobid-xml"},"referenced_works_count":92,"referenced_works":["https://openalex.org/W2012561700","https://openalex.org/W2150884987","https://openalex.org/W2404161646","https://openalex.org/W2581624817","https://openalex.org/W2606974598","https://openalex.org/W2612431505","https://openalex.org/W2732004306","https://openalex.org/W2739874095","https://openalex.org/W2769041395","https://openalex.org/W2888482885","https://openalex.org/W2892192320","https://openalex.org/W2895604144","https://openalex.org/W2902293828","https://openalex.org/W2921572111","https://openalex.org/W2959716049","https://openalex.org/W2963748441","https://openalex.org/W2970641574","https://openalex.org/W3002104146","https://openalex.org/W3008254571","https://openalex.org/W3039017601","https://openalex.org/W3092257341","https://openalex.org/W3101474014","https://openalex.org/W3119866685","https://openalex.org/W3122241445","https://openalex.org/W3152956381","https://openalex.org/W3156470785","https://openalex.org/W3164972323","https://openalex.org/W3169130206","https://openalex.org/W3185341429","https://openalex.org/W3195577433","https://openalex.org/W3210904070","https://openalex.org/W4221143046","https://openalex.org/W4221161695","https://openalex.org/W4221163578","https://openalex.org/W4225999997","https://openalex.org/W4239390603","https://openalex.org/W4284680369","https://openalex.org/W4285129823","https://openalex.org/W4287891464","https://openalex.org/W4297633153","https://openalex.org/W4303648559","https://openalex.org/W4304194220","https://openalex.org/W4307311835","https://openalex.org/W4308757044","https://openalex.org/W4310998073","https://openalex.org/W4319653585","https://openalex.org/W4365563505","https://openalex.org/W4376122773","https://openalex.org/W4379468930","https://openalex.org/W4385571076","https://openalex.org/W4385571098","https://openalex.org/W4385571189","https://openalex.org/W4385573504","https://openalex.org/W4385573512","https://openalex.org/W4385825586","https://openalex.org/W4387163503","https://openalex.org/W4387355600","https://openalex.org/W4387725649","https://openalex.org/W4388514997","https://openalex.org/W4388787977","https://openalex.org/W4389403907","https://openalex.org/W4389518340","https://openalex.org/W4389520201","https://openalex.org/W4389520255","https://openalex.org/W4389539730","https://openalex.org/W4390783809","https://openalex.org/W4391710071","https://openalex.org/W4392539324","https://openalex.org/W4392576573","https://openalex.org/W4392822465","https://openalex.org/W4392971790","https://openalex.org/W4393161007","https://openalex.org/W4394708144","https://openalex.org/W4394948080","https://openalex.org/W4395443209","https://openalex.org/W4398757466","https://openalex.org/W4399511904","https://openalex.org/W4400141368","https://openalex.org/W4400531953","https://openalex.org/W4401043635","https://openalex.org/W4401157551","https://openalex.org/W4404954357","https://openalex.org/W4411453026","https://openalex.org/W4415795422","https://openalex.org/W4415795709","https://openalex.org/W4416036114","https://openalex.org/W6857065707","https://openalex.org/W7119001154","https://openalex.org/W7133200236","https://openalex.org/W7133219791","https://openalex.org/W7133220558","https://openalex.org/W7133239970"],"related_works":["https://openalex.org/W7214888546","https://openalex.org/W4407570965","https://openalex.org/W7202136322","https://openalex.org/W7220721796","https://openalex.org/W7151095642","https://openalex.org/W7138457546","https://openalex.org/W7163888232","https://openalex.org/W7214761223","https://openalex.org/W7166272683","https://openalex.org/W4417077618"],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,64],"(LLMs)":[3],"are":[4],"increasingly":[5],"used":[6],"in":[7],"applications":[8],"where":[9],"LLM":[10,25,48,112,136,146],"inputs":[11],"may":[12,31],"span":[13],"many":[14],"different":[15,29,35,98],"tasks.":[16],"Recent":[17],"work":[18,69],"has":[19],"found":[20],"that":[21,87,144,162],"the":[22,135,138,155],"choice":[23],"of":[24,95,110],"is":[26,73],"consequential,":[27],"and":[28,114,130,161],"LLMs":[30],"be":[32],"good":[33],"for":[34,51,127,166],"input":[36],"samples.":[37],"Prior":[38],"approaches":[39],"have":[40],"thus":[41],"explored":[42],"how":[43],"engineers":[44],"might":[45],"select":[46],"an":[47],"to":[49,75,134,170],"use":[50],"each":[52,128,132],"sample":[53,133],"(i.e.":[54],"_routing_).":[55],"While":[56],"existing":[57],"routing":[58,85,167],"methods":[59],"mostly":[60],"require":[61],"training":[62],"auxiliary":[63],"on":[65,158],"human-annotated":[66],"data,":[67],"our":[68],"explores":[70],"whether":[71],"it":[72],"possible":[74],"perform":[76],"_unsupervised_":[77],"routing.":[78],"We":[79,142],"propose":[80],"SMOOTHIE,":[81],"a":[82,93,102],"weak":[83],"supervision-inspired":[84],"approach":[86],"requires":[88],"no":[89],"labeled":[90],"data.":[91],"Given":[92],"set":[94],"outputs":[96,113],"from":[97],"LLMs,":[99],"SMOOTHIE":[100,163],"constructs":[101],"latent":[103],"variable":[104],"graphical":[105,120],"model":[106,151,157],"over":[107],"embedding":[108],"representations":[109],"observable":[111],"unknown":[115],"\u201ctrue\u201d":[116],"outputs.":[117],"Using":[118],"this":[119],"model,":[121],"we":[122],"estimate":[123],"sample-dependent":[124],"quality":[125,152],"scores":[126],"LLM,":[129],"route":[131],"with":[137,149],"highest":[139],"corresponding":[140],"score.":[141],"find":[143],"SMOOTHIE\u2019s":[145],"quality-scores":[147],"correlate":[148],"ground-truth":[150],"(correctly":[153],"identifying":[154],"optimal":[156],"9/14":[159],"tasks),":[160],"outperforms":[164],"baselines":[165],"by":[168],"up":[169],"10":[171],"points":[172],"accuracy.":[173]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2025-01-09T00:00:00"}
