{"id":"https://openalex.org/W4416035877","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.71","title":"ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion","display_name":"ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416035877","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.71"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.71","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.71","pdf_url":"https://aclanthology.org/2025.findings-emnlp.71.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.71.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120308931","display_name":"Rana Shahroz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rana Shahroz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Dongwen Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongwen Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009249610","display_name":"Pingzhi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pingzhi Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100437130","display_name":"Kai Wang","orcid":"https://orcid.org/0000-0003-4204-3984"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086723183","display_name":"Tianlong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianlong Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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":0.15845659,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1357","last_page":"1370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.1746000051498413,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.1746000051498413,"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/T11448","display_name":"Face recognition and analysis","score":0.05339999869465828,"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/T10028","display_name":"Topic Modeling","score":0.052799999713897705,"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/stability","display_name":"Stability (learning theory)","score":0.3398999869823456},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.29429998993873596},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2777999937534332},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.26930001378059387},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.26409998536109924}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4715000092983246},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3813000023365021},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3675999939441681},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3294999897480011},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29429998993873596},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.71","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.71","pdf_url":"https://aclanthology.org/2025.findings-emnlp.71.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.71","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.71","pdf_url":"https://aclanthology.org/2025.findings-emnlp.71.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":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416035877.pdf","grobid_xml":"https://content.openalex.org/works/W4416035877.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Parameter":[0],"generation":[1,90],"has":[2],"emerged":[3],"as":[4],"a":[5,65,76],"novel":[6,66,77],"paradigm":[7],"for":[8,32],"neural":[9,17],"network":[10,18],"development,":[11],"offering":[12],"an":[13],"alternative":[14],"to":[15,106,143],"traditional":[16],"training":[19],"by":[20],"synthesizing":[21],"high-quality":[22,134],"model":[23,82],"weights":[24],"directly.In":[25],"the":[26,89],"context":[27],"of":[28,91],"Low-Rank":[29],"Adaptation":[30],"(LoRA)":[31],"evolving":[33,100],"(i.e.,":[34],"constantly":[35],"updated)":[36],"large":[37],"language":[38,116],"models":[39],"(LLMs),":[40],"this":[41,60],"approach":[42,103],"promises":[43],"efficient":[44],"adaptation":[45],"without":[46],"costly":[47],"retraining.However,":[48],"existing":[49],"methods":[50],"face":[51],"critical":[52],"limitations":[53],"in":[54],"simultaneously":[55],"achieving":[56],"scalability":[57],"and":[58,84,109,121],"controllability.In":[59],"paper,":[61],"we":[62,129],"introduce":[63],"ORAL,":[64],"conditional":[67],"recurrent":[68],"diffusion":[69],"framework":[70],"that":[71,80,95,131,137],"addresses":[72],"these":[73],"challenges.ORAL":[74],"incorporates":[75],"conditioning":[78],"mechanism":[79],"integrates":[81],"architecture":[83],"textual":[85],"task":[86],"specifications,":[87],"enabling":[88],"task-specific":[92],"LoRA":[93,135],"parameters":[94,136],"can":[96],"seamlessly":[97],"transfer":[98],"across":[99,114],"foundation":[101],"models.Our":[102],"successfully":[104],"scales":[105],"billions-of-parameter":[107],"LLMs":[108],"maintains":[110],"controllability.Through":[111],"extensive":[112],"experiments":[113],"seven":[115],"tasks,":[117,120],"four":[118],"vision":[119],"three":[122],"multimodal":[123],"tasks":[124],"using":[125],"five":[126],"pre-trained":[127],"LLMs,":[128],"demonstrate":[130],"ORAL":[132],"generates":[133],"achieve":[138],"comparable":[139],"or":[140],"superior":[141],"performance":[142],"vanilla":[144],"trained":[145],"counterparts.":[146]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}