{"id":"https://openalex.org/W7114778146","doi":"https://doi.org/10.48550/arxiv.2512.08786","title":"A Systematic Evaluation of Preference Aggregation in Federated RLHF for Pluralistic Alignment of LLMs","display_name":"A Systematic Evaluation of Preference Aggregation in Federated RLHF for Pluralistic Alignment of LLMs","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W7114778146","doi":"https://doi.org/10.48550/arxiv.2512.08786"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.08786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.08786","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":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.2512.08786","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Srewa, Mahmoud","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srewa, Mahmoud","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhao, Tianyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Tianyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Elmalaki, Salma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elmalaki, Salma","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":0.3849000036716461,"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":0.3849000036716461,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.13860000669956207,"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":0.09269999712705612,"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/preference","display_name":"Preference","score":0.5383999943733215},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5335000157356262},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.49390000104904175},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.46059998869895935},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.44040000438690186},{"id":"https://openalex.org/keywords/aggregation-problem","display_name":"Aggregation problem","score":0.33239999413490295},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.3312000036239624},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.30219998955726624}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7128000259399414},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5383999943733215},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5335000157356262},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.44040000438690186},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38269999623298645},{"id":"https://openalex.org/C32172795","wikidata":"https://www.wikidata.org/wiki/Q4692266","display_name":"Aggregation problem","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.32330000400543213},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.29820001125335693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2939000129699707},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.28200000524520874},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C58546491","wikidata":"https://www.wikidata.org/wiki/Q1150207","display_name":"Competitive advantage","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.2574999928474426},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.08786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.08786","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.08786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.08786","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":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":{"This":[0,133],"paper":[1],"addresses":[2],"the":[3,39,68],"challenge":[4],"of":[5],"aligning":[6],"large":[7],"language":[8],"models":[9],"(LLMs)":[10],"with":[11],"diverse":[12,28,144],"human":[13,52],"preferences":[14],"within":[15],"federated":[16,56],"learning":[17],"(FL)":[18],"environments,":[19],"where":[20],"standard":[21,82],"methods":[22],"often":[23],"fail":[24],"to":[25],"adequately":[26],"represent":[27],"viewpoints.":[29],"We":[30],"introduce":[31,91],"a":[32,92,103,115,136,148],"comprehensive":[33],"evaluation":[34],"framework":[35],"that":[36,96,120],"systematically":[37],"assesses":[38],"trade-off":[40],"between":[41],"alignment":[42,106,131],"quality":[43],"and":[44,63,67,88,90,146,155],"fairness":[45,127],"when":[46],"using":[47,114],"different":[48],"aggregation":[49,84],"strategies":[50],"for":[51,139,151],"preferences.":[53],"In":[54],"our":[55,121],"setting,":[57],"each":[58],"group":[59],"locally":[60],"evaluates":[61],"rollouts":[62],"produces":[64],"reward":[65,83],"signals,":[66],"server":[69],"aggregates":[70],"these":[71],"group-level":[72],"rewards":[73],"without":[74],"accessing":[75],"any":[76],"raw":[77],"data.":[78],"Specifically,":[79],"we":[80],"evaluate":[81],"techniques":[85],"(min,":[86],"max,":[87],"average)":[89],"novel":[93],"adaptive":[94,122],"scheme":[95],"dynamically":[97],"adjusts":[98],"preference":[99],"weights":[100],"based":[101],"on":[102,110],"group's":[104],"historical":[105],"performance.":[107],"Our":[108],"experiments":[109],"question-answering":[111],"(Q/A)":[112],"tasks":[113],"PPO-based":[116],"RLHF":[117],"pipeline":[118],"demonstrate":[119],"approach":[123],"consistently":[124],"achieves":[125],"superior":[126],"while":[128],"maintaining":[129],"competitive":[130],"scores.":[132],"work":[134],"offers":[135],"robust":[137],"methodology":[138],"evaluating":[140],"LLM":[141],"behavior":[142],"across":[143],"populations":[145],"provides":[147],"practical":[149],"solution":[150],"developing":[152],"truly":[153],"pluralistic":[154],"fairly":[156],"aligned":[157],"models.":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-12-11T00:00:00"}