{"id":"https://openalex.org/W7135075872","doi":"https://doi.org/10.48550/arxiv.2603.10287","title":"MultiwayPAM: Multiway Partitioning Around Medoids for LLM-as-a-Judge Score Analysis","display_name":"MultiwayPAM: Multiway Partitioning Around Medoids for LLM-as-a-Judge Score Analysis","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135075872","doi":"https://doi.org/10.48550/arxiv.2603.10287"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.10287","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10287","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.2603.10287","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126370663","display_name":"Chihiro Watanabe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Watanabe, Chihiro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128836089","display_name":"Jingyu Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jingyu","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":0.7817000150680542,"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":0.7817000150680542,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.057100001722574234,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.05299999937415123,"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/medoid","display_name":"Medoid","score":0.8547999858856201},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6855000257492065},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5339999794960022},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4077000021934509},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3978999853134155},{"id":"https://openalex.org/keywords/k-medoids","display_name":"k-medoids","score":0.3400999903678894}],"concepts":[{"id":"https://openalex.org/C63085389","wikidata":"https://www.wikidata.org/wiki/Q4287912","display_name":"Medoid","level":3,"score":0.8547999858856201},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6855000257492065},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5001999735832214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48330000042915344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46950000524520874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4332999885082245},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C65932786","wikidata":"https://www.wikidata.org/wiki/Q3191282","display_name":"k-medoids","level":5,"score":0.3400999903678894},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2978000044822693},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.10287","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10287","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.2603.10287","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10287","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5495455861091614}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LLM-as-a-Judge":[0,34,83],"is":[1],"a":[2,18,44,76,81,102,124],"flexible":[3],"framework":[4],"for":[5,14,91,120,160],"text":[6,20],"evaluation,":[7],"which":[8,109],"allows":[9],"us":[10],"to":[11,74,80,156],"obtain":[12],"scores":[13,90],"the":[15,26,62,89,114,118,130,140,149,157],"quality":[16],"of":[17,38,47,52,64,94,123,142,151],"given":[19,82,125],"from":[21],"various":[22],"perspectives":[23],"by":[24,68,133,153],"changing":[25],"prompt":[27],"template.":[28],"Two":[29],"main":[30],"challenges":[31],"in":[32],"using":[33],"are":[35,88],"computational":[36],"cost":[37],"LLM":[39,54,70],"inference,":[40],"especially":[41],"when":[42],"evaluating":[43],"large":[45],"number":[46],"texts,":[48],"and":[49,60,97,117],"inherent":[50],"bias":[51,66],"an":[53,69],"evaluator.":[55],"To":[56],"address":[57],"these":[58],"issues":[59],"reveal":[61],"structure":[63],"score":[65,84,158],"caused":[67],"evaluator,":[71],"we":[72,100,110,135],"propose":[73],"apply":[75],"tensor":[77,104],"clustering":[78,105],"method":[79,106],"tensor,":[85],"whose":[86],"entries":[87],"different":[92],"combinations":[93],"questions,":[95],"answerers,":[96],"evaluators.":[98],"Specifically,":[99],"develop":[101],"new":[103],"MultiwayPAM,":[107,134],"with":[108],"can":[111,136],"simultaneously":[112],"estimate":[113],"cluster":[115],"membership":[116,141],"medoids":[119,131],"each":[121,143],"mode":[122],"data":[126],"tensor.":[127],"By":[128],"observing":[129],"obtained":[132],"gain":[137],"knowledge":[138],"about":[139],"question/answerer/evaluator":[144],"cluster.":[145],"We":[146],"experimentally":[147],"show":[148],"effectiveness":[150],"MultiwayPAM":[152],"applying":[154],"it":[155],"tensors":[159],"two":[161],"practical":[162],"datasets.":[163]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-13T00:00:00"}