{"id":"https://openalex.org/W7161961999","doi":"https://doi.org/10.48550/arxiv.2605.21063","title":"APM: Evaluating Style Personalization in LLMs with Arbitrary Preference Mappings","display_name":"APM: Evaluating Style Personalization in LLMs with Arbitrary Preference Mappings","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161961999","doi":"https://doi.org/10.48550/arxiv.2605.21063"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21063","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.21063","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136707925","display_name":"Philipp Spohn","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spohn, Philipp","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015190855","display_name":"Leander Girrbach","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Girrbach, Leander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136699294","display_name":"Zeynep Akata","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akata, Zeynep","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/T10203","display_name":"Recommender Systems and Techniques","score":0.5608999729156494,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.5608999729156494,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.06440000236034393,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.051500000059604645,"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/personalization","display_name":"Personalization","score":0.9028000235557556},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.6538000106811523},{"id":"https://openalex.org/keywords/formality","display_name":"Formality","score":0.6158000230789185},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5612000226974487},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.49459999799728394},{"id":"https://openalex.org/keywords/style","display_name":"Style (visual arts)","score":0.4943000078201294},{"id":"https://openalex.org/keywords/conflation","display_name":"Conflation","score":0.3544999957084656},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.351500004529953}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.9028000235557556},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7177000045776367},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6538000106811523},{"id":"https://openalex.org/C2777159308","wikidata":"https://www.wikidata.org/wiki/Q1757948","display_name":"Formality","level":2,"score":0.6158000230789185},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5612000226974487},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.49459999799728394},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.41200000047683716},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36649999022483826},{"id":"https://openalex.org/C130440534","wikidata":"https://www.wikidata.org/wiki/Q14946528","display_name":"Conflation","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3176000118255615},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.31040000915527344},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2824000120162964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2712000012397766},{"id":"https://openalex.org/C3017893058","wikidata":"https://www.wikidata.org/wiki/Q999278","display_name":"User satisfaction","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21063","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.21063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21063","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Typical":[0],"LLM":[1,59],"responses":[2],"tend":[3],"to":[4,36,101,173],"follow":[5],"a":[6,92,177],"default":[7],"style,":[8],"even":[9],"though":[10],"users":[11,43],"often":[12],"have":[13],"distinct":[14],"preferences":[15,39,52,102,125],"regarding":[16],"tone,":[17],"verbosity,":[18],"and":[19,57,112,122,138,142,147,168],"formality":[20],"that":[21,97,152,184],"they":[22],"do":[23],"not":[24,54],"explicitly":[25],"state":[26],"in":[27,185],"their":[28],"prompts.":[29],"Evaluating":[30],"whether":[31],"personalization":[32,63,140,189],"methods":[33,141,195],"can":[34],"adapt":[35,135],"these":[37,70],"implicit":[38],"is":[40,113,154],"challenging,":[41,191],"since":[42],"typically":[44],"provide":[45],"prompts":[46],"rather":[47],"than":[48],"reference":[49],"responses,":[50],"style":[51],"are":[53],"factually":[55],"verifiable,":[56],"reference-free":[58],"judges":[60],"may":[61],"conflate":[62],"with":[64,163],"general":[65],"response":[66,87,104],"quality.":[67],"To":[68],"address":[69],"challenges,":[71],"we":[72,134],"introduce":[73],"the":[74,155,164],"Arbitrary":[75],"Preference":[76],"Mapping":[77],"(APM)":[78],"benchmark,":[79],"which":[80],"decouples":[81],"user":[82,99],"attributes":[83,100],"(e.g.":[84,89],"enthusiastic)":[85],"from":[86,126],"principles":[88],"persuasive)":[90],"via":[91],"hidden,":[93],"randomized":[94],"mapping":[95],"$\\mathbf{C}$":[96,107],"maps":[98],"about":[103],"traits.":[105],"Because":[106],"carries":[108],"no":[109],"semantic":[110],"content":[111],"resampled":[114],"across":[115],"runs,":[116],"models":[117],"cannot":[118],"exploit":[119],"stereotypical":[120],"associations":[121],"must":[123],"infer":[124],"conversation":[127],"history.":[128],"Using":[129],"this":[130,186],"unbiased":[131],"evaluation":[132,182],"methodology,":[133],"retrieval-augmented,":[136],"prompt-optimization,":[137],"routing":[139,153],"evaluate":[143],"them":[144],"on":[145],"Llama-3.1-8B":[146],"Qwen-3.5-27B.":[148],"Our":[149,180],"results":[150],"show":[151,196],"most":[156],"reliable":[157],"approach,":[158],"while":[159],"RAG":[160],"only":[161],"improves":[162],"stronger":[165],"base":[166],"LLM,":[167],"soft":[169],"prompt":[170],"optimization":[171],"fails":[172],"improve":[174],"significantly":[175],"over":[176],"non-personalized":[178],"baseline.":[179],"extensive":[181],"reveals":[183],"realistic":[187],"setting,":[188],"remains":[190],"but":[192],"our":[193],"adapted":[194],"promise.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-22T00:00:00"}