{"id":"https://openalex.org/W7139941223","doi":"https://doi.org/10.48550/arxiv.2603.18113","title":"VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models","display_name":"VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7139941223","doi":"https://doi.org/10.48550/arxiv.2603.18113"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18113","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18113","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.2603.18113","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130233345","display_name":"Hefei Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Hefei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130253919","display_name":"Le Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Le","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130228308","display_name":"Yu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130222074","display_name":"Min Hou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130235679","display_name":"Han Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130242010","display_name":"Zhen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130245718","display_name":"Meng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Meng","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":0.3262999951839447,"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":0.3262999951839447,"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":0.10580000281333923,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.08349999785423279,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6477000117301941},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5792999863624573},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5645999908447266},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.516700029373169},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.46239998936653137},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4408999979496002},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.4284999966621399},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.39899998903274536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042999863624573},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6477000117301941},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5792999863624573},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5645999908447266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353999733924866},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.516700029373169},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4853000044822693},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.46239998936653137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4438000023365021},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.4284999966621399},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18113","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18113","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.2603.18113","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18113","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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7366428971290588}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"large":[1],"language":[2],"models":[3,68,177],"(LLMs)":[4],"increasingly":[5],"shape":[6],"content":[7],"generation,":[8],"interaction,":[9],"and":[10,51,112,118,146,166,190,205,214],"decision-making":[11],"across":[12,92,194],"the":[13,105,135,139,169],"Web,":[14],"aligning":[15,35],"them":[16],"with":[17,199],"human":[18,39,94],"values":[19,195],"has":[20],"become":[21],"a":[22,115,129],"central":[23],"objective":[24],"in":[25,110,123,162],"trustworthy":[26],"AI.":[27],"This":[28],"challenge":[29],"becomes":[30],"even":[31],"more":[32],"pronounced":[33],"when":[34],"multiple,":[36],"potentially":[37],"conflicting":[38],"values.":[40,95],"Although":[41],"recent":[42],"approaches,":[43],"such":[44],"as":[45],"reward":[46],"reweighting,":[47],"prompt-based":[48],"supervised":[49],"fine-tuning,":[50],"model":[52],"merging,":[53],"attempt":[54],"to":[55,88,172,196],"tackle":[56],"multi-value":[57,102,201,218],"alignment,":[58],"they":[59],"still":[60],"face":[61],"two":[62],"major":[63],"limitations:":[64],"(1)":[65],"training":[66],"separate":[67],"for":[69],"each":[70,143,163],"value":[71,77,108,130,164],"combination":[72],"is":[73],"prohibitively":[74],"expensive;":[75],"(2)":[76],"conflicts":[78,213],"substantially":[79],"degrade":[80],"alignment":[81,103,219],"performance.":[82,202],"These":[83],"limitations":[84],"make":[85],"it":[86],"difficult":[87],"achieve":[89],"favorable":[90],"trade-offs":[91],"diverse":[93],"To":[96],"address":[97],"these":[98,188],"challenges,":[99],"we":[100,185],"revisit":[101],"from":[104],"perspective":[106],"of":[107,142],"consistency":[109,131],"data":[111,116,171],"propose":[113],"VC-soup,":[114],"filtering":[117,193],"parameter":[119],"merging":[120],"framework":[121],"grounded":[122],"value-consistent":[124,175],"learning.":[125],"We":[126,155],"first":[127],"design":[128],"metric":[132],"based":[133],"on":[134,168],"cosine":[136],"similarity":[137],"between":[138],"reward-gap":[140],"vector":[141],"preference":[144,160],"pair":[145],"an":[147],"all-ones":[148],"vector,":[149],"which":[150],"quantifies":[151],"its":[152],"cross-value":[153],"coherence.":[154],"then":[156],"filter":[157],"out":[158],"low-consistency":[159],"pairs":[161],"dataset":[165],"train":[167],"remaining":[170],"obtain":[173,197],"smooth,":[174],"policy":[176],"that":[178,209],"better":[179],"preserve":[180],"linear":[181],"mode":[182],"connectivity.":[183],"Finally,":[184],"linearly":[186],"combine":[187],"policies":[189],"apply":[191],"Pareto":[192],"solutions":[198],"balanced":[200],"Extensive":[203],"experiments":[204],"theoretical":[206],"analysis":[207],"demonstrate":[208],"VC-soup":[210],"effectively":[211],"mitigates":[212],"consistently":[215],"outperforms":[216],"existing":[217],"methods.":[220]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-21T00:00:00"}