{"id":"https://openalex.org/W7111119147","doi":"https://doi.org/10.48550/arxiv.2512.06239","title":"LOCUS: A System and Method for Low-Cost Customization for Universal Specialization","display_name":"LOCUS: A System and Method for Low-Cost Customization for Universal Specialization","publication_year":2025,"publication_date":"2025-12-06","ids":{"openalex":"https://openalex.org/W7111119147","doi":"https://doi.org/10.48550/arxiv.2512.06239"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.06239","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.06239","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.06239","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sundararaman, Dhanasekar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sundararaman, Dhanasekar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Keying","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Keying","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Xiong, Wayne","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Wayne","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Garg, Aashna","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garg, Aashna","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T10028","display_name":"Topic Modeling","score":0.3050000071525574,"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":0.3050000071525574,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.13030000030994415,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.10350000113248825,"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/personalization","display_name":"Personalization","score":0.7908999919891357},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6385999917984009},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5985999703407288},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.37940001487731934},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.36800000071525574},{"id":"https://openalex.org/keywords/locus","display_name":"Locus (genetics)","score":0.3677999973297119}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7908999919891357},{"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/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6385999917984009},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5985999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48730000853538513},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43959999084472656},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C84597430","wikidata":"https://www.wikidata.org/wiki/Q106227","display_name":"Locus (genetics)","level":3,"score":0.3677999973297119},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3625999987125397},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C72414096","wikidata":"https://www.wikidata.org/wiki/Q1367461","display_name":"Mass customization","level":3,"score":0.2655999958515167}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.06239","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.06239","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.06239","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.06239","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"LOCUS":[2,40],"(LOw-cost":[3],"Customization":[4],"for":[5],"Universal":[6],"Specialization),":[7],"a":[8,34,45],"pipeline":[9],"that":[10],"consumes":[11],"few-shot":[12],"data":[13,27,43,54],"to":[14],"streamline":[15],"the":[16,107],"construction":[17],"and":[18,29,56,74,89],"training":[19,50],"of":[20,37,98,106,120],"NLP":[21],"models":[22,58,95],"through":[23],"targeted":[24],"retrieval,":[25],"synthetic":[26],"generation,":[28,55],"parameter-efficient":[30],"tuning.":[31],"With":[32],"only":[33],"small":[35],"number":[36],"labeled":[38],"examples,":[39],"discovers":[41],"pertinent":[42],"in":[44],"broad":[46],"repository,":[47],"synthesizes":[48],"additional":[49],"samples":[51],"via":[52],"in-context":[53],"fine-tunes":[57],"using":[59,103],"either":[60],"full":[61],"or":[62],"low-rank":[63],"(LoRA)":[64],"parameter":[65],"adaptation.":[66],"Our":[67,92],"approach":[68],"targets":[69],"named":[70],"entity":[71],"recognition":[72],"(NER)":[73],"text":[75],"classification":[76],"(TC)":[77],"benchmarks,":[78],"consistently":[79],"outperforming":[80],"strong":[81],"baselines":[82],"(including":[83],"GPT-4o)":[84],"while":[85,102],"substantially":[86],"lowering":[87],"costs":[88],"model":[90],"sizes.":[91],"resultant":[93],"memory-optimized":[94],"retain":[96],"99%":[97],"fully":[99],"fine-tuned":[100],"accuracy":[101],"barely":[104],"5%":[105],"memory":[108],"footprint,":[109],"also":[110],"beating":[111],"GPT-4o":[112],"on":[113],"several":[114],"benchmarks":[115],"with":[116],"less":[117],"than":[118],"1%":[119],"its":[121],"parameters.":[122]},"counts_by_year":[],"updated_date":"2025-12-10T02:49:46.989445","created_date":"2025-12-10T00:00:00"}
