{"id":"https://openalex.org/W7151804345","doi":"https://doi.org/10.48550/arxiv.2604.05267","title":"Do Domain-specific Experts exist in MoE-based LLMs?","display_name":"Do Domain-specific Experts exist in MoE-based LLMs?","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151804345","doi":"https://doi.org/10.48550/arxiv.2604.05267"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05267","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05267","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05267","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133177025","display_name":"Giang Do","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Do, Giang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133179265","display_name":"Hung Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Hung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133181056","display_name":"Truyen Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Truyen","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":0.3440000116825104,"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":0.3440000116825104,"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/T10028","display_name":"Topic Modeling","score":0.11710000038146973,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.09149999916553497,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7135999798774719},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6139000058174133},{"id":"https://openalex.org/keywords/empirical-evidence","display_name":"Empirical evidence","score":0.4120999872684479},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.38089999556541443},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.335099995136261}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7135999798774719},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6139000058174133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5709999799728394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45559999346733093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45489999651908875},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36309999227523804},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.326200008392334},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C76509639","wikidata":"https://www.wikidata.org/wiki/Q918036","display_name":"Race (biology)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.27410000562667847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05267","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05267","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05267","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05267","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":"Preprint"},"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":{"In":[0,61],"the":[1,8,45,82,101],"era":[2],"of":[3,10,47,103,115],"Large":[4],"Language":[5],"Models":[6],"(LLMs),":[7],"Mixture":[9,114],"Experts":[11,116],"(MoE)":[12],"architecture":[13],"has":[14],"emerged":[15],"as":[16],"an":[17],"effective":[18],"approach":[19],"for":[20,100],"training":[21],"extremely":[22],"large":[23],"models":[24],"with":[25],"improved":[26],"computational":[27],"efficiency.":[28],"This":[29],"success":[30],"builds":[31],"upon":[32],"extensive":[33],"prior":[34],"research":[35,59],"aimed":[36],"at":[37,176],"enhancing":[38],"expert":[39],"specialization":[40],"in":[41,77],"MoE-based":[42,78,88,131,145],"LLMs.":[43],"However,":[44],"nature":[46],"such":[48],"specializations":[49],"and":[50,96,127,133,150,160],"how":[51],"they":[52],"can":[53],"be":[54],"systematically":[55],"interpreted":[56],"remain":[57],"open":[58],"challenges.":[60],"this":[62,66,108],"work,":[63],"we":[64,84,110],"investigate":[65],"gap":[67],"by":[68],"posing":[69],"a":[70,118],"fundamental":[71],"question:":[72],"\\textit{Do":[73],"domain-specific":[74,104],"experts":[75],"exist":[76],"LLMs?}":[79],"To":[80],"answer":[81],"question,":[83],"evaluate":[85],"ten":[86],"advanced":[87,143],"LLMs":[89,132,146],"ranging":[90],"from":[91],"3.8B":[92],"to":[93],"120B":[94],"parameters":[95],"provide":[97],"empirical":[98],"evidence":[99],"existence":[102],"experts.":[105],"Building":[106],"on":[107,141],"finding,":[109],"propose":[111],"\\textbf{Domain":[112],"Steering":[113],"(DSMoE)},":[117],"training-free":[119],"framework":[120],"that":[121,154],"introduces":[122],"zero":[123],"additional":[124,169],"inference":[125,165],"cost":[126,166],"outperforms":[128],"both":[129,148],"well-trained":[130],"strong":[134,158],"baselines,":[135],"including":[136],"Supervised":[137],"Fine-Tuning":[138],"(SFT).":[139],"Experiments":[140],"four":[142],"open-source":[144],"across":[147],"target":[149],"non-target":[151],"domains":[152],"demonstrate":[153],"our":[155],"method":[156],"achieves":[157],"performance":[159],"robust":[161],"generalization":[162],"without":[163],"increasing":[164],"or":[167],"requiring":[168],"retraining.":[170],"Our":[171],"implementation":[172],"is":[173],"publicly":[174],"available":[175],"https://github.com/giangdip2410/Domain-specific-Experts.":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}