{"id":"https://openalex.org/W7123946233","doi":"https://doi.org/10.48550/arxiv.2601.06180","title":"MixDPO: Modeling Preference Strength for Pluralistic Alignment","display_name":"MixDPO: Modeling Preference Strength for Pluralistic Alignment","publication_year":2026,"publication_date":"2026-01-07","ids":{"openalex":"https://openalex.org/W7123946233","doi":"https://doi.org/10.48550/arxiv.2601.06180"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.06180","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06180","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.06180","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122986677","display_name":"Saki Imai","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Imai, Saki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Heydari, Pedram","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heydari, Pedram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073469194","display_name":"Anthony Sicilia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sicilia, Anthony","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122949312","display_name":"Asteria Kaeberlein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaeberlein, Asteria","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122927868","display_name":"Katherine Atwell","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atwell, Katherine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122957316","display_name":"Malihe Alikhani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alikhani, Malihe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5122986677"],"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.16269999742507935,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.16269999742507935,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.10719999670982361,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.1071000024676323,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8295000195503235},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6395000219345093},{"id":"https://openalex.org/keywords/mixed-logit","display_name":"Mixed logit","score":0.5873000025749207},{"id":"https://openalex.org/keywords/revealed-preference","display_name":"Revealed preference","score":0.484499990940094},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4659000039100647},{"id":"https://openalex.org/keywords/preference-learning","display_name":"Preference learning","score":0.421999990940094},{"id":"https://openalex.org/keywords/preference-elicitation","display_name":"Preference elicitation","score":0.41990000009536743}],"concepts":[{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.8295000195503235},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6395000219345093},{"id":"https://openalex.org/C95057490","wikidata":"https://www.wikidata.org/wiki/Q6883984","display_name":"Mixed logit","level":3,"score":0.5873000025749207},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5059999823570251},{"id":"https://openalex.org/C2779110102","wikidata":"https://www.wikidata.org/wiki/Q1323737","display_name":"Revealed preference","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44110000133514404},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.421999990940094},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.41990000009536743},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3894999921321869},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37220001220703125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3675999939441681},{"id":"https://openalex.org/C32172795","wikidata":"https://www.wikidata.org/wiki/Q4692266","display_name":"Aggregation problem","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C2781043087","wikidata":"https://www.wikidata.org/wiki/Q939761","display_name":"Preference theory","level":3,"score":0.3472000062465668},{"id":"https://openalex.org/C190669063","wikidata":"https://www.wikidata.org/wiki/Q5282043","display_name":"Discrete choice","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.31299999356269836},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.27469998598098755},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.06180","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06180","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":"doi:10.48550/arxiv.2601.06180","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06180","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":"article"},"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.48398053646087646}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Preference":[0,59,66],"based":[1],"alignment":[2,76,107],"objectives":[3,43,77],"implicitly":[4],"assume":[5],"that":[6,68],"all":[7],"human":[8,48],"preferences":[9,84],"are":[10,85],"expressed":[11,86],"with":[12,118,125],"equal":[13],"strength.":[14,73],"In":[15],"practice,":[16],"however,":[17],"preference":[18,72,95,128,132],"strength":[19,137],"varies":[20],"across":[21,87],"individuals":[22],"and":[23,32],"contexts":[24],"--":[25],"a":[26,62],"phenomenon":[27],"established":[28],"in":[29,71,81,123],"behavioral":[30],"economics":[31],"discrete":[33],"choice":[34],"theory.":[35],"This":[36],"mismatch":[37],"limits":[38],"the":[39,119],"ability":[40],"of":[41,64],"existing":[42],"to":[44,78],"faithfully":[45],"capture":[46,79],"heterogeneous":[47],"judgments.":[49],"Inspired":[50],"by":[51],"this":[52],"literature,":[53],"we":[54],"introduce":[55],"Mixed":[56],"Logit":[57],"Direct":[58,65],"Optimization":[60,67],"(MixDPO),":[61],"generalization":[63],"models":[69],"variation":[70],"MixDPO":[74,92,104,130],"enables":[75],"heterogeneity":[80,133],"how":[82],"strongly":[83],"training":[88],"examples.":[89],"We":[90,139],"evaluate":[91],"on":[93,111],"three":[94],"datasets":[96],"using":[97],"two":[98],"open-weight":[99],"language":[100],"models.":[101],"Across":[102],"datasets,":[103],"improves":[105],"aggregate":[106],"performance":[108],"(+11.2":[109],"points":[110],"Pythia-2.8B)":[112],"while":[113],"preserving":[114],"subgroup":[115],"level":[116],"preferences,":[117],"largest":[120],"gains":[121],"appearing":[122],"settings":[124],"higher":[126],"inferred":[127],"heterogeneity.":[129],"makes":[131],"explicit":[134],"through":[135],"learned":[136],"distributions.":[138],"release":[140],"our":[141],"code":[142],"for":[143],"reproducibility.":[144]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-01-14T00:00:00"}
