{"id":"https://openalex.org/W7133332553","doi":"https://doi.org/10.48550/arxiv.2603.01865","title":"CyclicJudge: Mitigating Judge Bias Efficiently in LLM-based Evaluation","display_name":"CyclicJudge: Mitigating Judge Bias Efficiently in LLM-based Evaluation","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133332553","doi":"https://doi.org/10.48550/arxiv.2603.01865"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.01865","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127929938","display_name":"Ziyi Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Ziyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128007851","display_name":"Olivier Tieleman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tieleman, Olivier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112704052","display_name":"Alexey Bukhtiyarov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bukhtiyarov, Alexey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103109768","display_name":"Jinghong Chen","orcid":"https://orcid.org/0009-0007-7783-4336"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jinghong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.5307999849319458,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.5307999849319458,"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/T13643","display_name":"Artificial Intelligence in Law","score":0.03370000049471855,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.03020000085234642,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6815000176429749},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6406999826431274},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6136000156402588},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.44859999418258667},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.36899998784065247},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.3490999937057495}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6815000176429749},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6406999826431274},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6136000156402588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5619000196456909},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.47850000858306885},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33309999108314514},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3052000105381012},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C63002673","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring rule","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.25099998712539673},{"id":"https://openalex.org/C89715816","wikidata":"https://www.wikidata.org/wiki/Q7915763","display_name":"Variance decomposition of forecast errors","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.01865","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.01865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01865","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.01865","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4614467918872833}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LLM-as-judge":[0],"evaluation":[1,130],"has":[2],"become":[3],"standard":[4],"practice":[5],"for":[6,90],"open-ended":[7],"model":[8,37],"assessment;":[9],"however,":[10],"judges":[11,80],"exhibit":[12],"systematic":[13],"biases":[14,29],"that":[15,39,59],"cannot":[16],"be":[17,86],"averaged":[18],"out":[19],"by":[20],"increasing":[21],"the":[22,36,87,98,101,107,119],"number":[23],"of":[24,79,109,121],"scenarios":[25],"or":[26],"generations.":[27],"These":[28],"are":[30,41,52],"often":[31],"similar":[32],"in":[33,46],"magnitude":[34],"to":[35,43,81,85],"differences":[38],"benchmarks":[40],"designed":[42],"detect,":[44],"resulting":[45],"unreliable":[47],"rankings":[48],"when":[49],"single-judge":[50,110],"evaluations":[51],"used.":[53],"We":[54],"introduce":[55],"a":[56,76,91],"variance":[57,63],"decomposition":[58],"partitions":[60],"benchmark":[61],"score":[62,99],"into":[64],"scenario,":[65],"generation,":[66],"judge,":[67],"and":[68,95,116,128],"residual":[69],"components.":[70],"Based":[71],"on":[72,114],"this":[73],"analysis,":[74],"CyclicJudge,":[75],"round-robin":[77],"assignment":[78],"scenarios,":[82],"is":[83],"demonstrated":[84],"optimal":[88],"strategy":[89],"fixed":[92],"judge":[93],"panel":[94,102],"judge-call":[96],"budget:":[97],"recovers":[100],"mean":[103],"exactly":[104],"while":[105],"matching":[106],"cost":[108],"evaluation.":[111],"Empirical":[112],"results":[113],"MT-Bench":[115],"MindEval":[117],"validate":[118],"effectiveness":[120],"CyclicJudge":[122],"as":[123],"predicted,":[124],"across":[125],"both":[126],"general-purpose":[127],"domain-specific":[129],"settings.":[131]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}
