{"id":"https://openalex.org/W7136463706","doi":"https://doi.org/10.48550/arxiv.2603.12658","title":"Continual Learning in Large Language Models: Methods, Challenges, and Opportunities","display_name":"Continual Learning in Large Language Models: Methods, Challenges, and Opportunities","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7136463706","doi":"https://doi.org/10.48550/arxiv.2603.12658"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12658","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12658","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.2603.12658","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129425884","display_name":"Hongyang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hongyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129533015","display_name":"Zhongwu Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zhongwu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012969013","display_name":"Hongfei Ye","orcid":"https://orcid.org/0000-0002-7966-2466"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Hongfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023566199","display_name":"Kunchi Li","orcid":"https://orcid.org/0000-0001-9874-3571"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Kunchi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5079659938","display_name":"Xuemin Lin","orcid":"https://orcid.org/0000-0003-2396-7225"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Xuemin","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8198000192642212,"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":0.8198000192642212,"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":0.020500000566244125,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.014700000174343586,"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/forgetting","display_name":"Forgetting","score":0.7465999722480774},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5281000137329102},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.40610000491142273},{"id":"https://openalex.org/keywords/lifelong-learning","display_name":"Lifelong learning","score":0.3698999881744385},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.34150001406669617},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.34049999713897705},{"id":"https://openalex.org/keywords/core-competency","display_name":"Core competency","score":0.3212999999523163}],"concepts":[{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.7465999722480774},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6166999936103821},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5281000137329102},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.44589999318122864},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C108771440","wikidata":"https://www.wikidata.org/wiki/Q368475","display_name":"Lifelong learning","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35420000553131104},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.3418000042438507},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C164065428","wikidata":"https://www.wikidata.org/wiki/Q1201929","display_name":"Core competency","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31940001249313354},{"id":"https://openalex.org/C186528591","wikidata":"https://www.wikidata.org/wiki/Q619544","display_name":"Body of knowledge","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.27889999747276306},{"id":"https://openalex.org/C189708586","wikidata":"https://www.wikidata.org/wiki/Q1504425","display_name":"Systematic review","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C43540301","wikidata":"https://www.wikidata.org/wiki/Q689971","display_name":"Paradigm shift","level":2,"score":0.25099998712539673},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12658","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12658","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.2603.12658","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12658","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":[{"score":0.5030235052108765,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Continual":[0],"learning":[1,205],"(CL)":[2],"has":[3],"emerged":[4],"as":[5],"a":[6,42,86,193],"pivotal":[7],"paradigm":[8,34],"to":[9,15,18,36,120,179],"enable":[10],"large":[11],"language":[12,207],"models":[13],"(LLMs)":[14],"dynamically":[16],"adapt":[17],"evolving":[19],"knowledge":[20,137,167,184],"and":[21,61,70,84,93,113,124,136,172,190,200],"sequential":[22],"tasks":[23,171],"while":[24,152],"mitigating":[25],"catastrophic":[26],"forgetting-a":[27],"critical":[28,94],"limitation":[29],"of":[30,45,67,90,96,183],"the":[31,64,91,180],"static":[32],"pre-training":[33],"inherent":[35],"modern":[37],"LLMs.":[38,101],"This":[39,148,175],"survey":[40,149],"presents":[41],"comprehensive":[43],"overview":[44],"CL":[46,98,112,146],"methodologies":[47],"tailored":[48],"for":[49,100,144,196,206],"LLMs,":[50],"structured":[51,194],"around":[52],"three":[53],"core":[54,108],"training":[55],"stages:":[56],"continual":[57,59,62],"pre-training,":[58],"fine-tuning,":[60],"alignment.Beyond":[63],"canonical":[65],"taxonomy":[66],"rehearsal-,":[68],"regularization-,":[69],"architecture-based":[71],"methods,":[72],"we":[73,105],"further":[74],"subdivide":[75],"each":[76],"category":[77],"by":[78],"its":[79],"distinct":[80],"forgetting":[81,134],"mitigation":[82],"mechanisms":[83],"conduct":[85],"rigorous":[87],"comparative":[88],"analysis":[89,128],"adaptability":[92],"improvements":[95],"traditional":[97,114],"methods":[99,154],"In":[102],"doing":[103],"so,":[104],"explicitly":[106],"highlight":[107],"distinctions":[109],"between":[110],"LLM":[111,186],"machine":[115],"learning,":[116],"particularly":[117],"with":[118,141,192],"respect":[119],"scale,":[121],"parameter":[122],"efficiency,":[123,139],"emergent":[125],"capabilities.":[126],"Our":[127],"covers":[129],"essential":[130],"evaluation":[131],"metrics,":[132],"including":[133],"rates":[135],"transfer":[138],"along":[140],"emerging":[142],"benchmarks":[143],"assessing":[145],"performance.":[147],"reveals":[150],"that":[151],"current":[153,198],"demonstrate":[155],"promising":[156],"results":[157],"in":[158,164,203],"specific":[159],"domains,":[160],"fundamental":[161],"challenges":[162],"persist":[163],"achieving":[165],"seamless":[166],"integration":[168],"across":[169],"diverse":[170],"temporal":[173],"scales.":[174],"systematic":[176],"review":[177],"contributes":[178],"growing":[181],"body":[182],"on":[185],"adaptation,":[187],"providing":[188],"researchers":[189],"practitioners":[191],"framework":[195],"understanding":[197],"achievements":[199],"future":[201],"opportunities":[202],"lifelong":[204],"models.":[208]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-17T00:00:00"}