{"id":"https://openalex.org/W7148637371","doi":"https://doi.org/10.48550/arxiv.2604.02048","title":"Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models","display_name":"Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7148637371","doi":"https://doi.org/10.48550/arxiv.2604.02048"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02048","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02048","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.2604.02048","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111251789","display_name":"Issa Sugiura","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sugiura, Issa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132857934","display_name":"Keito Sasagawa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sasagawa, Keito","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022881767","display_name":"Keisuke Nakao","orcid":"https://orcid.org/0000-0002-5260-1110"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nakao, Keisuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132904811","display_name":"Koki Maeda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maeda, Koki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132895288","display_name":"Ziqi Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Ziqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062273504","display_name":"Zhishen Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhishen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073732915","display_name":"Shuhei Kurita","orcid":"https://orcid.org/0000-0001-7415-3120"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kurita, Shuhei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132838983","display_name":"Yusuke Oda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oda, Yusuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132907263","display_name":"Ryoko Tokuhisa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tokuhisa, Ryoko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132861300","display_name":"Daisuke Kawahara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kawahara, Daisuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132847500","display_name":"Naoaki Okazaki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okazaki, Naoaki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":11,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9936000108718872,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9936000108718872,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0013000000035390258,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.0010000000474974513,"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/obstacle","display_name":"Obstacle","score":0.6014000177383423},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5450999736785889},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5157999992370605},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.47130000591278076},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4007999897003174},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.38580000400543213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784500002861023},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.6014000177383423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5806999802589417},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.47130000591278076},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3772999942302704},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2896000146865845},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26739999651908875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02048","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02048","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.2604.02048","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02048","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":[{"id":"https://metadata.un.org/sdg/4","score":0.4557507634162903,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Developing":[0],"vision-language":[1],"models":[2],"(VLMs)":[3],"that":[4,125],"generalize":[5],"across":[6,85,143],"diverse":[7,14,86],"tasks":[8,147],"requires":[9],"large-scale":[10],"training":[11,172],"datasets":[12,19,46],"with":[13,130,158,173],"content.":[15],"In":[16,66],"English,":[17],"such":[18,114],"are":[20],"typically":[21],"constructed":[22],"by":[23],"aggregating":[24],"and":[25,52,63,104,107,120,148,179,188],"curating":[26],"numerous":[27],"existing":[28,92],"visual":[29],"question":[30],"answering":[31],"(VQA)":[32],"resources.":[33],"However,":[34],"this":[35,67],"strategy":[36],"does":[37,160],"not":[38,161],"readily":[39],"extend":[40],"to":[41,59,78,171],"other":[42],"languages,":[43],"where":[44],"VQA":[45,93,109],"remain":[47],"limited":[48],"in":[49,140],"both":[50],"scale":[51],"domain":[53],"coverage,":[54],"posing":[55],"a":[56,126],"major":[57],"obstacle":[58],"building":[60],"high-quality":[61],"multilingual":[62],"non-English":[64],"VLMs.":[65],"work,":[68],"we":[69,95,182],"introduce":[70],"Jagle,":[71],"the":[72,184],"largest":[73],"Japanese":[74,136,145],"multimodal":[75],"post-training":[76],"dataset":[77],"date,":[79],"comprising":[80],"approximately":[81],"9.2":[82],"million":[83],"instances":[84],"tasks.":[87],"Rather":[88],"than":[89],"relying":[90],"on":[91,135],"datasets,":[94],"collect":[96],"heterogeneous":[97],"source":[98],"data,":[99],"including":[100],"images,":[101],"image-text":[102],"pairs,":[103],"PDF":[105],"documents,":[106],"generate":[108],"pairs":[110],"through":[111],"multiple":[112],"strategies":[113],"as":[115],"VLM-based":[116],"QA":[117],"generation,":[118],"translation,":[119],"text":[121],"rendering.":[122],"Experiments":[123],"demonstrate":[124],"2.2B":[127],"model":[128],"trained":[129,186],"Jagle":[131,157],"achieves":[132],"strong":[133],"performance":[134,169],"tasks,":[137],"surpassing":[138],"InternVL3.5-2B":[139],"average":[141],"score":[142],"ten":[144],"evaluation":[146],"approaching":[149],"within":[150],"five":[151],"points":[152],"of":[153],"Qwen3-VL-2B-Instruct.":[154],"Furthermore,":[155],"combining":[156],"FineVision":[159,174],"degrade":[162],"English":[163,168],"performance;":[164],"instead,":[165],"it":[166],"improves":[167],"compared":[170],"alone.":[175],"To":[176],"facilitate":[177],"reproducibility":[178],"future":[180],"research,":[181],"release":[183],"dataset,":[185],"models,":[187],"code.":[189]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-04T00:00:00"}