{"id":"https://openalex.org/W7150992815","doi":"https://doi.org/10.48550/arxiv.2604.02338","title":"LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning","display_name":"LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning","publication_year":2026,"publication_date":"2026-02-01","ids":{"openalex":"https://openalex.org/W7150992815","doi":"https://doi.org/10.48550/arxiv.2604.02338"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02338","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.02338","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133011789","display_name":"Md Kowsher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kowsher, Md","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133043320","display_name":"Haris Mansoor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mansoor, Haris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059594952","display_name":"Nusrat Jahan Prottasha","orcid":"https://orcid.org/0000-0001-9446-1745"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prottasha, Nusrat Jahan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062903281","display_name":"\u00d6zlem \u00d6zmen Garibay","orcid":"https://orcid.org/0000-0001-9215-694X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garibay, Ozlem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126926465","display_name":"Victor Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Victor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133007317","display_name":"Zhengping Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Zhengping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133006941","display_name":"Chen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chen","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":0.382099986076355,"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":0.382099986076355,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.19280000030994415,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1379999965429306,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6265000104904175},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5498999953269958},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.5116999745368958},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4839000105857849},{"id":"https://openalex.org/keywords/router","display_name":"Router","score":0.47350001335144043},{"id":"https://openalex.org/keywords/expert-system","display_name":"Expert system","score":0.4440000057220459},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.4318000078201294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6811000108718872},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6265000104904175},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5498999953269958},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5468000173568726},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.5116999745368958},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C2775896111","wikidata":"https://www.wikidata.org/wiki/Q642560","display_name":"Router","level":2,"score":0.47350001335144043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46160000562667847},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.4440000057220459},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C2778218555","wikidata":"https://www.wikidata.org/wiki/Q250423","display_name":"Lime","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2669000029563904},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2667999863624573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02338","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.02338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02338","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"MoE-PEFT":[0,176],"methods":[1],"combine":[2],"Mixture":[3,37],"of":[4,38,52],"Experts":[5],"with":[6,24,66,117,143],"parameter-efficient":[7],"fine-tuning":[8],"for":[9],"multi-task":[10,141],"adaptation,":[11],"but":[12],"require":[13],"separate":[14,53],"adapters":[15],"per":[16,97],"expert":[17,25,42,68,71,128],"causing":[18],"trainable":[19,165],"parameters":[20,72,94,166],"to":[21,30,75,162,169,174],"scale":[22],"linearly":[23],"count":[26],"and":[27,62,88,110,126,149,167],"limiting":[28],"applicability":[29],"adapter-based":[31],"architectures.":[32],"We":[33],"propose":[34],"LiME":[35,55,80,120,153],"(Lightweight":[36],"Experts),":[39],"which":[40],"achieves":[41,154],"specialization":[43],"through":[44],"lightweight":[45,67],"modulation":[46,112],"rather":[47],"than":[48],"adapter":[49],"replication.":[50],"Instead":[51],"adapters,":[54],"uses":[56],"a":[57,139],"single":[58],"shared":[59],"PEFT":[60,77,116],"module":[61],"modulates":[63],"its":[64],"output":[65],"vectors,":[69],"reducing":[70],"while":[73,159],"generalizing":[74],"any":[76],"method.":[78],"Notably,":[79],"introduces":[81],"zero-parameter":[82],"routing":[83,125,134],"by":[84],"leveraging":[85],"existing":[86],"frozen":[87],"adapted":[89],"representations":[90],"eliminating":[91],"learned":[92],"router":[93],"typically":[95],"required":[96],"layer.":[98],"Theoretically,":[99],"we":[100],"prove":[101],"that":[102,152],"(i)":[103],"more":[104,107],"experts":[105],"preserve":[106],"task-relevant":[108],"information":[109],"(ii)":[111],"approximates":[113],"full":[114],"expert-specific":[115],"bounded":[118],"error.":[119],"further":[121],"incorporates":[122],"n-gram":[123],"windowed":[124],"adaptive":[127],"selection":[129],"(Auto":[130],"Top-K)":[131],"based":[132],"on":[133,137],"confidence.":[135],"Experiments":[136],"MMT-47,":[138],"multimodal":[140],"benchmark":[142],"47":[144],"tasks":[145],"spanning":[146],"text,":[147],"image,":[148],"video,":[150],"demonstrate":[151],"competitive":[155],"or":[156],"superior":[157],"performance":[158],"using":[160],"up":[161,168],"4x":[163],"fewer":[164],"29%":[170],"faster":[171],"training":[172],"compared":[173],"corresponding":[175],"baselines.":[177]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-07T00:00:00"}
