{"id":"https://openalex.org/W4415909416","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2104","title":"Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging","display_name":"Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4415909416","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2104"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.2104","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2104","pdf_url":"https://aclanthology.org/2026.findings-acl.2104.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.2104.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006591088","display_name":"Tiancheng Hu","orcid":"https://orcid.org/0009-0006-7354-1088"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tiancheng Hu","raw_affiliation_strings":["University of Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120262267","display_name":"Benjamin Minixhofer","orcid":null},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Benjamin Minixhofer","raw_affiliation_strings":["University of Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073413742","display_name":"Nigel Collier","orcid":"https://orcid.org/0000-0002-7230-4164"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nigel Collier","raw_affiliation_strings":["University of Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I241749"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01772655,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"42405","last_page":"42422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.39570000767707825,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.39570000767707825,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.06310000270605087,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.04349999874830246,"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/process","display_name":"Process (computing)","score":0.6517999768257141},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.6263999938964844},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5766000151634216},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5134000182151794},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.4853000044822693},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.43869999051094055},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.39649999141693115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.682699978351593},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6517999768257141},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.6263999938964844},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5766000151634216},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5134000182151794},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4625999927520752},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33219999074935913},{"id":"https://openalex.org/C76956256","wikidata":"https://www.wikidata.org/wiki/Q27610560","display_name":"Process modeling","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C2778571376","wikidata":"https://www.wikidata.org/wiki/Q1355821","display_name":"Frontier","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2678999900817871},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.2104","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2104","pdf_url":"https://aclanthology.org/2026.findings-acl.2104.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.17426","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2510.17426","pdf_url":"https://arxiv.org/pdf/2510.17426","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.17426","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.17426","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:10.18653/v1/2026.findings-acl.2104","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2104","pdf_url":"https://aclanthology.org/2026.findings-acl.2104.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1690597291","display_name":null,"funder_award_id":"#OPP1144","funder_id":"https://openalex.org/F4320323264","funder_display_name":"Gates Cambridge Trust"},{"id":"https://openalex.org/G5477836236","display_name":"Cambridge University Development Office in the United States Inc (CUDOUS) - to endow a global scholarship program at the post-baccalaureate level for academically gifted students","funder_award_id":"OPP1144","funder_id":"https://openalex.org/F4320306137","funder_display_name":"Bill and Melinda Gates Foundation"},{"id":"https://openalex.org/G6770599008","display_name":null,"funder_award_id":"OPP1144","funder_id":"https://openalex.org/F4320314731","funder_display_name":"UK Research and Innovation"},{"id":"https://openalex.org/G963556061","display_name":null,"funder_award_id":"ST/AIRR/I-A-I/1023","funder_id":"https://openalex.org/F4320334632","funder_display_name":"Science and Technology Facilities Council"}],"funders":[{"id":"https://openalex.org/F4320306137","display_name":"Bill and Melinda Gates Foundation","ror":"https://ror.org/0456r8d26"},{"id":"https://openalex.org/F4320314707","display_name":"Government of the United Kingdom","ror":"https://ror.org/05wnh3t63"},{"id":"https://openalex.org/F4320314731","display_name":"UK Research and Innovation","ror":"https://ror.org/001aqnf71"},{"id":"https://openalex.org/F4320320360","display_name":"University of Bristol","ror":"https://ror.org/0524sp257"},{"id":"https://openalex.org/F4320321848","display_name":"Cambridge Trust","ror":"https://ror.org/05sprqy15"},{"id":"https://openalex.org/F4320323264","display_name":"Gates Cambridge Trust","ror":"https://ror.org/033sn5p83"},{"id":"https://openalex.org/F4320334632","display_name":"Science and Technology Facilities Council","ror":"https://ror.org/057g20z61"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415909416.pdf","grobid_xml":"https://content.openalex.org/works/W4415909416.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"\"alignment":[1],"tax\"":[2],"of":[3,21,99],"post-training":[4],"is":[5,56],"typically":[6],"framed":[7],"as":[8],"a":[9,18,42,48,58,90],"drop":[10],"in":[11],"task":[12],"accuracy.We":[13],"show":[14],"it":[15],"also":[16],"involves":[17],"severe":[19],"loss":[20],"calibration,":[22],"making":[23],"models":[24,104],"overconfident,":[25],"less":[26,31],"reliable,":[27],"and":[28,52,109],"model":[29,87],"outputs":[30],"diverse.We":[32],"demonstrate":[33],"that":[34,62,69,85,105],"this":[35,55],"trade-off":[36],"can":[37],"be":[38],"navigated":[39],"effectively":[40],"via":[41],"simple":[43,86],"post-hoc":[44],"intervention:":[45],"interpolating":[46],"between":[47],"model's":[49],"weights":[50],"before":[51],"after":[53],"alignment.Crucially,":[54],"not":[57],"strict":[59],"trade-off.We":[60],"find":[61],"the":[63,78,96,100],"process":[64],"consistently":[65],"reveals":[66],"Pareto-optimal":[67],"interpolations-models":[68],"improve":[70],"accuracy":[71],"beyond":[72],"both":[73],"parents":[74],"while":[75],"substantially":[76],"recovering":[77],"calibration":[79],"lost":[80],"during":[81],"alignment.Our":[82],"work":[83],"demonstrates":[84],"merging":[88],"provides":[89],"computationally":[91],"efficient":[92],"method":[93],"for":[94],"mitigating":[95],"full":[97],"scope":[98],"alignment":[101],"tax,":[102],"yielding":[103],"are":[106],"more":[107,110],"capable":[108],"reliable.":[111],"1":[112]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-22T00:00:00"}
