{"id":"https://openalex.org/W7130579885","doi":"https://doi.org/10.1109/ictc66702.2025.11388812","title":"Beyond Full Fine-Tuning: Parameter-Efficient Adaptation for Large-Scale Natural Language Generation","display_name":"Beyond Full Fine-Tuning: Parameter-Efficient Adaptation for Large-Scale Natural Language Generation","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W7130579885","doi":"https://doi.org/10.1109/ictc66702.2025.11388812"},"language":null,"primary_location":{"id":"doi:10.1109/ictc66702.2025.11388812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11388812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5111208866","display_name":"Emily Jimin Roh","orcid":"https://orcid.org/0009-0008-0013-6342"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Emily Jimin Roh","raw_affiliation_strings":["Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093460594","display_name":"Soohyun Park","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soohyun Park","raw_affiliation_strings":["Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103015771","display_name":"Joongheon Kim","orcid":"https://orcid.org/0000-0002-1794-6076"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Joongheon Kim","raw_affiliation_strings":["Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I197347611"],"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":"807","last_page":"809"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8179000020027161,"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":0.8179000020027161,"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":0.06870000064373016,"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":0.019700000062584877,"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/natural-language-generation","display_name":"Natural language generation","score":0.8901000022888184},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.7239999771118164},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6140000224113464},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5403000116348267},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4860999882221222},{"id":"https://openalex.org/keywords/prefix","display_name":"Prefix","score":0.37549999356269836}],"concepts":[{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.8901000022888184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7688999772071838},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.7239999771118164},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6140000224113464},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4860999882221222},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.453000009059906},{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.33500000834465027},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31630000472068787},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30169999599456787},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2639000117778778}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc66702.2025.11388812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11388812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2604345102","https://openalex.org/W4386212326","https://openalex.org/W4393147154","https://openalex.org/W4399533471","https://openalex.org/W4401113574","https://openalex.org/W4416018107"],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1,10],"models":[2],"(LLMs)":[3],"have":[4,46],"achieved":[5],"remarkable":[6],"performance":[7,117],"in":[8,34,139],"natural":[9],"generation":[11,131],"(NLG),":[12],"powering":[13],"applications":[14],"such":[15],"as":[16,103],"summarization,":[17],"dialogue":[18],"systems,":[19],"and":[20,36,65,81,95,101,130],"instruction":[21],"following.":[22],"However,":[23],"the":[24,51,126],"prohibitive":[25],"cost":[26],"of":[27,53,68],"full":[28,73,115],"model":[29],"fine-tuning":[30,43,71,116],"limits":[31],"their":[32],"applicability":[33],"multi-task":[35],"resource-constrained":[37],"settings.":[38],"To":[39],"address":[40],"this,":[41],"parameter-efficient":[42],"(PEFT)":[44],"techniques":[45],"been":[47],"proposed":[48],"to":[49],"reduce":[50],"number":[52],"trainable":[54],"parameters":[55],"while":[56,118],"maintaining":[57],"competitive":[58],"performance.":[59],"This":[60],"paper":[61],"provides":[62],"an":[63],"introduction":[64],"comparative":[66],"study":[67],"four":[69],"representative":[70],"strategies:":[72],"fine-tuning,":[74],"low-rank":[75],"adaptation":[76],"(LoRA),":[77],"Sparse":[78],"LoRA":[79],"(SoRA)":[80],"prefix":[82],"tuning.":[83],"We":[84],"evaluate":[85],"these":[86],"approaches":[87],"on":[88],"standard":[89],"NLG":[90,141],"tasks,":[91],"which":[92],"includes":[93],"summarization":[94],"question":[96],"answering,":[97],"using":[98],"BLEU,":[99],"BERTScore,":[100],"ROUGE":[102],"evaluation":[104],"metrics.":[105],"Experimental":[106],"results":[107],"demonstrate":[108],"that":[109],"PEFT":[110],"methods":[111],"can":[112],"achieve":[113],"near":[114],"significantly":[119],"reducing":[120],"parameter":[121],"overhead.":[122],"Our":[123],"findings":[124],"highlight":[125],"trade-offs":[127],"between":[128],"efficiency":[129],"quality,":[132],"offering":[133],"practical":[134],"guidance":[135],"for":[136],"deploying":[137],"LLMs":[138],"real-world":[140],"scenarios.":[142]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-20T00:00:00"}