{"id":"https://openalex.org/W7155975158","doi":"https://doi.org/10.48550/arxiv.2604.22345","title":"Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization","display_name":"Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7155975158","doi":"https://doi.org/10.48550/arxiv.2604.22345"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22345","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.2604.22345","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114752575","display_name":"Weixu Zhang","orcid":"https://orcid.org/0009-0003-2000-7891"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weixu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134675154","display_name":"Ye Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Ye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134681430","display_name":"Changjiang Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Changjiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111065404","display_name":"Yuxing Tian","orcid":"https://orcid.org/0009-0008-0313-5089"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Yuxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134734070","display_name":"Zipeng Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134674553","display_name":"Linfeng Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Linfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021430632","display_name":"Jikun Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Jikun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134669217","display_name":"Hong Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134739180","display_name":"Xue Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134693831","display_name":"Haolun Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Haolun","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":0.2345999926328659,"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":0.2345999926328659,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.1687999963760376,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.05660000070929527,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.8391000032424927},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6887999773025513},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.6712999939918518},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4903999865055084},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4180000126361847},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41429999470710754},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.37779998779296875},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.37279999256134033}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8391000032424927},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6887999773025513},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6712999939918518},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6607000231742859},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4903999865055084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42089998722076416},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.39559999108314514},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3625999987125397},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.3084999918937683},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3073999881744385},{"id":"https://openalex.org/C106934330","wikidata":"https://www.wikidata.org/wiki/Q1971873","display_name":"Trait","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22345","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.2604.22345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22345","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"exhibit":[4],"strong":[5],"implicit":[6],"personalization":[7,94,147,156,178],"ability,":[8],"yet":[9],"most":[10],"existing":[11],"approaches":[12],"treat":[13],"this":[14,31],"behavior":[15],"as":[16],"a":[17,35,44,62,73,100,171],"black":[18],"box,":[19],"relying":[20],"on":[21,27,65,114,144],"prompt":[22],"engineering":[23],"or":[24],"fine":[25],"tuning":[26],"user":[28,54,115],"data.":[29],"In":[30],"work,":[32],"we":[33,120],"adopt":[34],"mechanistic":[36,172],"interpretability":[37],"perspective":[38],"and":[39,57,60,86,92,125,134,162,176],"hypothesize":[40],"the":[41,130],"existence":[42],"of":[43,47,174],"sparse":[45],"set":[46],"Preference":[48,70,80,101,127],"Heads,":[49,128],"attention":[50,107],"heads":[51],"that":[52,77],"encode":[53],"specific":[55],"stylistic":[56],"topical":[58],"preferences":[59],"exert":[61],"causal":[63,83,112],"influence":[64],"generation.":[66],"We":[67],"introduce":[68],"Differential":[69],"Steering":[71],"(DPS),":[72],"training":[74],"free":[75],"framework":[76],"(1)":[78],"identifies":[79],"Heads":[81],"through":[82],"masking":[84],"analysis":[85],"(2)":[87],"leverages":[88],"them":[89],"for":[90,105],"controllable":[91],"interpretable":[93],"at":[95],"inference":[96],"time.":[97],"DPS":[98,169],"computes":[99],"Contribution":[102],"Score":[103],"(PCS)":[104],"each":[106],"head,":[108],"directly":[109],"measuring":[110],"its":[111],"impact":[113],"aligned":[116,141],"outputs.":[117],"During":[118],"decoding,":[119],"contrast":[121],"model":[122],"predictions":[123],"with":[124],"without":[126],"amplifying":[129],"difference":[131],"between":[132],"personalized":[133],"generic":[135],"logits":[136],"to":[137],"selectively":[138],"strengthen":[139],"preference":[140],"continuations.":[142],"Experiments":[143],"widely":[145],"used":[146],"benchmarks":[148],"across":[149],"multiple":[150],"LLMs":[151],"demonstrate":[152],"consistent":[153],"gains":[154],"in":[155],"fidelity":[157],"while":[158],"preserving":[159],"content":[160],"coherence":[161],"low":[163],"computational":[164],"overhead.":[165],"Beyond":[166],"empirical":[167],"improvements,":[168],"provides":[170],"explanation":[173],"where":[175],"how":[177],"emerges":[179],"within":[180],"transformer":[181],"architectures.":[182],"Our":[183],"implementation":[184],"is":[185],"publicly":[186],"available.":[187]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-28T00:00:00"}