{"id":"https://openalex.org/W7125931323","doi":"https://doi.org/10.1109/smc58881.2025.11343222","title":"CDCO: Cross-Domain Contrastive Optimization Framework for Enhancing Multi-Task Learning in Small Pre-trained Language Models","display_name":"CDCO: Cross-Domain Contrastive Optimization Framework for Enhancing Multi-Task Learning in Small Pre-trained Language Models","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125931323","doi":"https://doi.org/10.1109/smc58881.2025.11343222"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124112392","display_name":"Yukun Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yukun Cao","raw_affiliation_strings":["Shanghai University of Electric Power,China,201306"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University of Electric Power,China,201306","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124123006","display_name":"Yongcheng He","orcid":null},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongcheng He","raw_affiliation_strings":["Shanghai University of Electric Power,China,201306"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University of Electric Power,China,201306","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124122176","display_name":"Niu Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu Gu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76902928,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4230","last_page":"4235"},"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":0.7766000032424927,"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":0.7766000032424927,"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/T10028","display_name":"Topic Modeling","score":0.05400000140070915,"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":0.019099999219179153,"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/adapter","display_name":"Adapter (computing)","score":0.6963000297546387},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5105000138282776},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.486299991607666},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4661000072956085},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4578999876976013},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.4348999857902527},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.40549999475479126},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3301999866962433}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7379999756813049},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.6963000297546387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.555400013923645},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5105000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49559998512268066},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4661000072956085},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4578999876976013},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.40549999475479126},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.2590999901294708}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2067067933","https://openalex.org/W2923014074","https://openalex.org/W2963961878","https://openalex.org/W4212939303","https://openalex.org/W4297971002","https://openalex.org/W4385569751","https://openalex.org/W4392560653","https://openalex.org/W4394994860","https://openalex.org/W4395000183","https://openalex.org/W4405868895"],"related_works":[],"abstract_inverted_index":{"The":[0],"adapter":[1,26,169,181],"technique":[2],"has":[3],"significantly":[4,202],"improved":[5],"the":[6,67,155,163,177,180,187],"performance":[7,56,75,82,204],"of":[8,158,179,191],"fine-tuning":[9],"pre-trained":[10],"language":[11],"models":[12,20,193],"(PLMs)":[13],"in":[14,51,76,100,194],"multi-task":[15,52,77,195],"settings,":[16],"especially":[17],"for":[18,58],"small":[19,192],"with":[21],"limited":[22],"resources.":[23],"However,":[24],"current":[25],"frameworks":[27],"typically":[28],"rely":[29],"on":[30,154,205],"single-task":[31],"data,":[32],"a":[33,38,149],"fixed":[34],"representation":[35,91],"space,":[36],"and":[37,93,105,143,189,210,218],"single":[39],"training":[40,106,117],"phase.":[41],"This":[42],"approach":[43],"limits":[44],"their":[45],"ability":[46],"to":[47,73,141,161],"generalize":[48],"across":[49,86,171],"domains":[50],"scenarios,":[53],"thus":[54,185],"restricting":[55],"improvements":[57],"smaller":[59],"models.":[60],"To":[61],"address":[62],"these":[63,159],"challenges,":[64],"we":[65],"propose":[66],"cross-domain":[68,150],"contrastive":[69,151],"optimization":[70,164],"(CDCO)":[71],"framework":[72],"enhance":[74],"learning.":[78,129],"CDCO":[79,97,109,131,175,201],"improves":[80,203],"model":[81],"by":[83,119],"asynchronously":[84],"co-optimizing":[85],"diverse":[87],"task":[88],"data":[89,102],"sources,":[90],"spaces,":[92],"multi-stage":[94,133],"structures.":[95],"Specifically,":[96],"introduces":[98,110],"innovations":[99],"both":[101],"sample":[103],"selection":[104],"strategy.":[107],"First,":[108],"out-of-domain":[111,125,208],"manifold":[112,128],"sampling":[113],"(ODMS),":[114],"which":[115],"enhances":[116],"diversity":[118],"selecting":[120],"challenging":[121],"hard-negative":[122],"samples":[123,138],"from":[124,139],"datasets":[126],"through":[127],"Second,":[130],"employs":[132],"asynchronous":[134],"co-optimization":[135],"(MAC),":[136],"mapping":[137],"ODMS":[140],"Euclidean":[142],"Poincar\u00e9":[144],"spaces.":[145],"Then,":[146],"it":[147],"constructs":[148],"loss":[152],"based":[153],"spatial":[156,173],"properties":[157],"distributions":[160],"guide":[162],"process.":[165],"By":[166],"sequentially":[167],"optimizing":[168],"layers":[170],"different":[172],"distributions,":[174],"maximizes":[176],"potential":[178],"while":[182],"mitigating":[183],"overfitting,":[184],"improving":[186],"adaptability":[188],"stability":[190],"environments.":[196],"Experimental":[197],"results":[198],"demonstrate":[199],"that":[200],"in-domain":[206],"(ID),":[207],"(OOD),":[209],"knowledge-intensive":[211],"(KI)":[212],"tasks,":[213],"confirming":[214],"its":[215],"broad":[216],"applicability":[217],"effectiveness.":[219]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}
