{"id":"https://openalex.org/W7151874200","doi":"https://doi.org/10.48550/arxiv.2604.05821","title":"CLEAR: Cross-Lingual Enhancement in Alignment via Reverse-training","display_name":"CLEAR: Cross-Lingual Enhancement in Alignment via Reverse-training","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151874200","doi":"https://doi.org/10.48550/arxiv.2604.05821"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05821","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.2604.05821","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033574608","display_name":"Seungyoon Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Seungyoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133224591","display_name":"Minhyuk Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Minhyuk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133216801","display_name":"Seongtae Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Seongtae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133161387","display_name":"Youngjoon Jang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jang, Youngjoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033588507","display_name":"Dongsuk Oh","orcid":"https://orcid.org/0000-0003-0969-6844"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oh, Dongsuk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133193162","display_name":"Heuiseok Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Heuiseok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":0.2264000028371811,"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":0.2264000028371811,"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":0.17190000414848328,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11789999902248383,"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/embedding","display_name":"Embedding","score":0.5115000009536743},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.484499990940094},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4629000127315521},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.43220001459121704},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4180999994277954},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4156999886035919},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4081999957561493}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7799000144004822},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5115000009536743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5095999836921692},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4697999954223633},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4629000127315521},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4180999994277954},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.3010999858379364},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2556000053882599},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05821","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.2604.05821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05821","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"multilingual":[1,146],"embedding":[2],"models":[3],"often":[4],"encounter":[5],"challenges":[6],"in":[7,46,60,103,117,126,133,145],"cross-lingual":[8,19,29,80,105,118],"scenarios":[9],"due":[10],"to":[11,37,74,91,123],"imbalanced":[12],"linguistic":[13],"resources":[14],"and":[15,43,98,154],"less":[16],"consideration":[17],"of":[18],"alignment":[20,40],"during":[21],"training.":[22],"Although":[23],"standardized":[24],"contrastive":[25],"learning":[26],"approaches":[27],"for":[28,151],"adaptation":[30],"are":[31],"widely":[32],"adopted,":[33],"they":[34],"may":[35],"struggle":[36],"capture":[38],"fundamental":[39],"between":[41,94],"languages":[42,48],"degrade":[44],"performance":[45,77,102,131],"well-aligned":[47],"such":[49],"as":[50,88],"English.":[51,134],"To":[52],"address":[53],"these":[54],"challenges,":[55],"we":[56],"propose":[57],"Cross-Lingual":[58],"Enhancement":[59],"Retrieval":[61],"via":[62],"Reverse-training":[63],"(CLEAR),":[64],"a":[65,70,89],"novel":[66],"loss":[67],"function":[68],"utilizing":[69],"reverse":[71],"training":[72],"scheme":[73],"improve":[75],"retrieval":[76,81,106],"across":[78],"diverse":[79],"scenarios.":[82],"CLEAR":[83,113,140],"leverages":[84],"an":[85],"English":[86],"passage":[87],"bridge":[90],"strengthen":[92],"alignments":[93],"the":[95,104,158],"target":[96],"language":[97],"English,":[99],"ensuring":[100],"robust":[101],"task.":[107],"Our":[108],"extensive":[109],"experiments":[110],"demonstrate":[111],"that":[112,139],"achieves":[114],"notable":[115],"improvements":[116],"scenarios,":[119],"with":[120],"gains":[121],"up":[122],"15%,":[124],"particularly":[125],"low-resource":[127],"languages,":[128],"while":[129],"minimizing":[130],"degradation":[132],"Furthermore,":[135],"our":[136],"findings":[137],"highlight":[138],"offers":[141],"promising":[142],"effectiveness":[143],"even":[144],"training,":[147],"suggesting":[148],"its":[149],"potential":[150],"broad":[152],"application":[153],"scalability.":[155],"We":[156],"release":[157],"code":[159],"at":[160],"https://github.com/dltmddbs100/CLEAR.":[161]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-09T00:00:00"}