{"id":"https://openalex.org/W7140598747","doi":"https://doi.org/10.48550/arxiv.2603.24124","title":"The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation","display_name":"The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7140598747","doi":"https://doi.org/10.48550/arxiv.2603.24124"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24124","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24124","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.2603.24124","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101963467","display_name":"Mingyi Liu","orcid":"https://orcid.org/0000-0001-7998-7415"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Mingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5101963467"],"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":0.6521000266075134,"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":0.6521000266075134,"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/T13629","display_name":"Text Readability and Simplification","score":0.09179999679327011,"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":0.08829999715089798,"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/discriminative-model","display_name":"Discriminative model","score":0.6180999875068665},{"id":"https://openalex.org/keywords/homogenization","display_name":"Homogenization (climate)","score":0.5507000088691711},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5163000226020813},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4699999988079071},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.46149998903274536},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.36480000615119934},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33869999647140503},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.32899999618530273}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6180999875068665},{"id":"https://openalex.org/C2778722038","wikidata":"https://www.wikidata.org/wiki/Q17030643","display_name":"Homogenization (climate)","level":3,"score":0.5507000088691711},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5163000226020813},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.46149998903274536},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4311000108718872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40560001134872437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39320001006126404},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.36480000615119934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32350000739097595},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3199000060558319},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C17859611","wikidata":"https://www.wikidata.org/wiki/Q10797615","display_name":"Equivocation","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2791999876499176},{"id":"https://openalex.org/C182049051","wikidata":"https://www.wikidata.org/wiki/Q17147155","display_name":"Transfer entropy","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C2779606619","wikidata":"https://www.wikidata.org/wiki/Q17092524","display_name":"Interchangeability","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C2834757","wikidata":"https://www.wikidata.org/wiki/Q4925424","display_name":"Monotone polygon","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25839999318122864}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24124","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24124","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.2603.24124","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24124","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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6770085096359253}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"RLHF-aligned":[0],"language":[1],"models":[2],"exhibit":[3],"response":[4,166],"homogenization:":[5],"on":[6,44,100,154],"TruthfulQA":[7],"(n=790),":[8],"40-79%":[9],"of":[10,60],"questions":[11],"produce":[12],"a":[13,179],"single":[14],"semantic":[15],"cluster":[16],"across":[17,115],"10":[18],"i.i.d.":[19],"samples.":[20],"On":[21],"affected":[22],"questions,":[23],"sampling-based":[24],"uncertainty":[25,185],"methods":[26],"have":[27],"zero":[28],"discriminative":[29],"power":[30],"(AUROC=0.500),":[31],"while":[32],"free":[33],"token":[34,47],"entropy":[35,48],"retains":[36],"signal":[37],"(0.603).":[38],"This":[39],"alignment":[40,105],"tax":[41,106],"is":[42,169],"task-dependent:":[43],"GSM8K":[45,190],"(n=500),":[46],"achieves":[49],"0.724":[50],"(Cohen's":[51],"d=0.81).":[52],"A":[53,78],"base-vs-instruct":[54],"ablation":[55,81],"confirms":[56,158],"the":[57,62,72,92],"causal":[58],"role":[59],"alignment:":[61],"base":[63],"model":[64,74,102,121,125],"shows":[65],"1.0%":[66],"single-cluster":[67],"rate":[68],"vs.":[69],"28.5%":[70],"for":[71],"instruct":[73],"(p":[75],"<":[76],"10^{-6}).":[77],"training":[79],"stage":[80],"(Base":[82],"0.0%":[83],"->":[84,87],"SFT":[85],"1.5%":[86],"DPO":[88],"4.0%":[89],"SCR)":[90,157],"localizes":[91],"cause":[93],"to":[94,194],"DPO,":[95],"not":[96],"SFT.":[97],"Cross-family":[98],"replication":[99],"four":[101],"families":[103,147],"reveals":[104],"severity":[107],"varies":[108],"by":[109,174],"family":[110],"and":[111,123,131,171],"scale.":[112],"We":[113],"validate":[114],"22":[116],"experiments,":[117],"5":[118],"benchmarks,":[119],"4":[120],"families,":[122],"3":[124],"scales":[126,137],"(3B-14B),":[127],"with":[128,143],"Jaccard,":[129],"embedding,":[130],"NLI-based":[132],"baselines":[133],"at":[134,196],"three":[135],"DeBERTa":[136],"(all":[138],"~0.51":[139],"AUROC).":[140],"Cross-embedder":[141],"validation":[142,153],"two":[144],"independent":[145],"embedding":[146],"rules":[148],"out":[149],"coupling":[150],"bias.":[151],"Cross-dataset":[152],"WebQuestions":[155],"(58.0%":[156],"generalization":[159],"beyond":[160],"TruthfulQA.":[161],"The":[162],"central":[163],"finding":[164],"--":[165,168],"homogenization":[167],"implementation-independent":[170],"label-free.":[172],"Motivated":[173],"this":[175],"diagnosis,":[176],"we":[177],"explore":[178],"cheapest-first":[180],"cascade":[181],"(UCBD)":[182],"over":[183],"orthogonal":[184],"signals.":[186],"Selective":[187],"prediction":[188],"raises":[189],"accuracy":[191],"from":[192],"84.4%":[193],"93.2%":[195],"50%":[197],"coverage;":[198],"weakly":[199],"dependent":[200],"boundaries":[201],"(|r|":[202],"<=":[203],"0.12)":[204],"enable":[205],"57%":[206],"cost":[207],"savings.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-27T00:00:00"}