{"id":"https://openalex.org/W4416034009","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1074","title":"NormAL LoRA: What is the perfect size?","display_name":"NormAL LoRA: What is the perfect size?","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034009","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1074"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1074","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1074","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1074.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: EMNLP 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.1074.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120308467","display_name":"Aastik","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aastik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120308468","display_name":"Topu Sai Meghana","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Topu Sai Meghana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120308469","display_name":"Chinmay Prakash Kulkarni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chinmay Prakash Kulkarni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5008389383","display_name":"Pragya Paramita Sahu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pragya Paramita Sahu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31112578,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"19716","last_page":"19731"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12002","display_name":"Computability, Logic, AI Algorithms","score":0.01140000019222498,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12002","display_name":"Computability, Logic, AI Algorithms","score":0.01140000019222498,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.006899999920278788,"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/T14330","display_name":"Library Science and Information Systems","score":0.006000000052154064,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/scalability","display_name":"Scalability","score":0.6087999939918518},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5346999764442444},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.5164999961853027},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.5076000094413757},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.45260000228881836},{"id":"https://openalex.org/keywords/transformative-learning","display_name":"Transformative learning","score":0.44519999623298645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7178999781608582},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6087999939918518},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.47510001063346863},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.45260000228881836},{"id":"https://openalex.org/C70587473","wikidata":"https://www.wikidata.org/wiki/Q7834111","display_name":"Transformative learning","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.375},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3278000056743622},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31679999828338623},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C72134830","wikidata":"https://www.wikidata.org/wiki/Q5166524","display_name":"Convexity","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27730000019073486}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.1074","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1074","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1074.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: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1074","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1074","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1074.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: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034009.pdf","grobid_xml":"https://content.openalex.org/works/W4416034009.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"are":[4],"pivotal":[5],"in":[6,65,77],"enabling":[7,132],"intelligent":[8],"experiences":[9],"across":[10,139],"various":[11],"applications,":[12],"from":[13],"summarization":[14],"to":[15,38,95],"advanced":[16],"content":[17],"organization":[18],"and":[19,32,69,79,135,142],"retrieval":[20],"functionalities.However,":[21],"deploying":[22],"LLMs":[23],"for":[24,42,56,71,101,131],"diverse":[25,140],"tasks":[26],"is":[27,107,111],"fundamentally":[28],"constrained":[29],"by":[30,118],"memory":[31],"compute":[33],"limitations,":[34],"making":[35,125],"it":[36,110],"impractical":[37],"fine-tune":[39],"separate":[40],"models":[41],"each":[43,72,102],"task.Parameter-Efficient":[44],"Fine-Tuning":[45],"(PEFT)":[46],"methods":[47],"like":[48],"Low-Rank":[49],"Adaptation":[50],"(LoRA)":[51],"offer":[52],"a":[53,88,128],"scalable":[54],"solution":[55],"multi-task":[57],"LLM":[58],"deployment.Despite":[59],"its":[60],"potential,":[61],"LoRA":[62,127],"faces":[63],"challenges":[64],"selecting":[66],"optimal":[67,99],"ranks":[68],"layers":[70],"task-model":[73],"pair,":[74],"often":[75],"resulting":[76],"inefficiencies":[78],"unnecessary":[80],"parameters.We":[81],"introduce":[82],"Norm":[83],"Adaptive":[84],"Localized":[85],"(NormAL)":[86],"LoRA,":[87],"novel":[89],"variant":[90],"that":[91],"employs":[92],"rank-norm":[93],"regularization":[94],"dynamically":[96],"determine":[97],"the":[98],"rank":[100],"weight":[103],"matrix,":[104],"ensuring":[105],"adaptation":[106],"concentrated":[108],"where":[109],"most":[112],"impactful.Our":[113],"approach":[114],"reduces":[115],"adapter":[116],"parameters":[117],"37%":[119],"while":[120],"preserving":[121],"full":[122],"finetuning":[123],"performance,":[124],"NormAL":[126],"transformative":[129],"tool":[130],"efficient,":[133],"scalable,":[134],"space-constrained":[136],"AI":[137],"deployments":[138],"industries":[141],"applications.We":[143],"release":[144],"our":[145],"code":[146],"here":[147],"1":[148],".":[149]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}