{"id":"https://openalex.org/W4391420732","doi":"https://doi.org/10.48550/arxiv.2401.16160","title":"LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs","display_name":"LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs","publication_year":2024,"publication_date":"2024-01-29","ids":{"openalex":"https://openalex.org/W4391420732","doi":"https://doi.org/10.48550/arxiv.2401.16160"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2401.16160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.16160","pdf_url":"https://arxiv.org/pdf/2401.16160","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.16160","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101499520","display_name":"Shaoxiang Chen","orcid":"https://orcid.org/0000-0002-7627-7124"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chen, Shaoxiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093836172","display_name":"Zequn Jie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie, Zequn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5093836173","display_name":"Lin Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101499520"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":6,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9330999851226807,"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"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9330999851226807,"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":0.9315999746322632,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9035999774932861,"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/computer-science","display_name":"Computer science","score":0.4225137233734131}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4225137233734131}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2401.16160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.16160","pdf_url":"https://arxiv.org/pdf/2401.16160","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2401.16160","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2401.16160","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":"pmh:oai:arXiv.org:2401.16160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.16160","pdf_url":"https://arxiv.org/pdf/2401.16160","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4391420732.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655","https://openalex.org/W2359140296"],"abstract_inverted_index":{"Instruction":[0],"finetuning":[1,90],"on":[2,125,207],"a":[3,14,61,81,106,126],"variety":[4],"of":[5,24,60,75,84,108,163],"image-text":[6],"instruction":[7,26,47,89,190],"data":[8,27,41,48,183],"is":[9,80,172],"the":[10,25,93,98,113,121,138,144,155,161,182,201,208,215,221],"key":[11],"to":[12,30,70,120,154],"obtaining":[13],"versatile":[15],"Multimodal":[16],"Large":[17],"Language":[18],"Model":[19],"(MLLM),":[20],"and":[21,116,146,195],"different":[22,34,135],"configurations":[23],"can":[28,53,212],"lead":[29],"finetuned":[31],"models":[32],"with":[33,165,192,219],"capabilities.":[35],"However,":[36],"we":[37,68,96],"have":[38],"discovered":[39],"that":[40,178],"conflicts":[42],"are":[43,141,149],"inevitable":[44],"when":[45,186],"mixing":[46,187],"from":[49,134],"distinct":[50,189],"domains,":[51],"which":[52,79],"result":[54],"in":[55],"performance":[56,198],"drops":[57],"for":[58,88,112,132],"tasks":[59],"specific":[62],"domain.":[63],"To":[64],"address":[65],"this":[66],"issue,":[67],"propose":[69],"apply":[71],"an":[72],"efficient":[73],"Mixture":[74,83],"Experts":[76,86],"(MoE)":[77],"design,":[78,168],"sparse":[82],"LoRA":[85,109,139,157],"(MoLE)":[87],"MLLMs.":[91],"Within":[92],"Transformer":[94],"layers,":[95],"extend":[97],"popular":[99],"Low-Rank":[100],"Adaption":[101],"(LoRA)":[102],"method":[103],"by":[104],"creating":[105],"set":[107],"experts":[110,140],"specifically":[111],"MLP":[114],"layer,":[115],"route":[117],"each":[118],"token":[119],"top-1":[122],"expert":[123],"based":[124],"routing":[127],"function,":[128],"allowing":[129],"adaptive":[130],"choices":[131],"tokens":[133],"domains.":[136],"Since":[137],"sparsely":[142],"activated,":[143],"training":[145],"inference":[147],"cost":[148],"kept":[150],"roughly":[151],"constant":[152],"compared":[153],"original":[156],"method.":[158],"By":[159],"replacing":[160],"plain-LoRA":[162,203,216],"LLaVA-1.5":[164],"our":[166,169],"MoE":[167],"final":[170],"model":[171],"named":[173],"LLaVA-MoLE.":[174],"Extensive":[175],"experiments":[176],"proved":[177],"LLaVA-MoLE":[179,211],"effectively":[180],"mitigates":[181],"conflict":[184],"issue":[185],"multiple":[188],"datasets":[191],"various":[193],"configurations,":[194],"achieves":[196],"consistent":[197],"gains":[199],"over":[200],"strong":[202],"baselines.":[204],"Most":[205],"importantly,":[206],"mixed":[209],"datasets,":[210],"even":[213],"outperform":[214],"baseline":[217],"trained":[218],"twice":[220],"samples.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2024-02-01T00:00:00"}
