{"id":"https://openalex.org/W7164384743","doi":"https://doi.org/10.48550/arxiv.2606.11643","title":"Improving Cross-Format Robustness in Language Models with Multi-Format Training","display_name":"Improving Cross-Format Robustness in Language Models with Multi-Format Training","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164384743","doi":"https://doi.org/10.48550/arxiv.2606.11643"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11643","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11643","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.2606.11643","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111193955","display_name":"June M. Liu","orcid":"https://orcid.org/0000-0001-9605-3606"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, June M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138460897","display_name":"Shaomian Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Shaomian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138477462","display_name":"He Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138394946","display_name":"Dingnan Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Dingnan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138438765","display_name":"Qing Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138481825","display_name":"Jun Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jun","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/T10028","display_name":"Topic Modeling","score":0.8406999707221985,"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/T10028","display_name":"Topic Modeling","score":0.8406999707221985,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.02290000021457672,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.022099999710917473,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/robustness","display_name":"Robustness (evolution)","score":0.7218999862670898},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4957999885082245},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4918000102043152},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.47530001401901245},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4731000065803528},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4174000024795532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7430999875068665},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7218999862670898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5080000162124634},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4957999885082245},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4918000102043152},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.47530001401901245},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4722999930381775},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4174000024795532},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36660000681877136},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.30799999833106995},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.26159998774528503}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11643","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11643","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.2606.11643","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11643","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":[{"score":0.6253437399864197,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"often":[3,114],"remain":[4],"sensitive":[5,164],"to":[6,34,160,165],"answer":[7,166],"format:":[8],"a":[9,36,56,157],"question":[10,42,87],"solved":[11],"correctly":[12],"in":[13,18],"one":[14],"form":[15],"may":[16],"fail":[17],"another":[19],"semantically":[20],"equivalent":[21,63],"form.":[22],"To":[23],"study":[24],"this":[25,124],"gap,":[26],"we":[27,133],"define":[28],"cross-format":[29,83],"robustness":[30],"as":[31],"the":[32,39,108,118,128,147,170],"extent":[33],"which":[35,53],"model":[37,129],"answers":[38],"same":[40],"underlying":[41],"consistently":[43,77],"across":[44,127],"formats.":[45],"We":[46,99],"then":[47],"compare":[48],"full-format":[49,121],"training":[50,59,109],"with":[51],"FormatMix,":[52],"expands":[54],"only":[55,104],"subset":[57],"of":[58,107,117,150],"items":[60],"into":[61,111],"multiple":[62,112],"formats":[64,113],"using":[65],"either":[66],"random":[67],"or":[68],"targeted":[69],"selection.":[70],"Across":[71],"GLM4":[72],"and":[73,82,94,123,131],"Llama-3.1,":[74],"multi-format":[75,154],"supervision":[76,89,144],"improves":[78],"both":[79],"task":[80],"performance":[81],"robustness,":[84],"whereas":[85],"Multiple-choice":[86],"(MCQ)-only":[88],"alone":[90],"brings":[91],"little":[92],"benefit":[93],"can":[95],"even":[96],"reduce":[97],"robustness.":[98,151],"further":[100],"find":[101],"that":[102,138],"expanding":[103],"about":[105],"30%":[106],"set":[110],"recovers":[115],"most":[116],"gain":[119],"from":[120],"training,":[122],"effect":[125],"appears":[126],"families":[130],"sizes":[132],"study.":[134],"These":[135],"results":[136],"suggest":[137],"format":[139,167],"diversity,":[140],"rather":[141],"than":[142],"additional":[143],"alone,":[145],"is":[146,156],"key":[148],"driver":[149],"That":[152],"lightweight":[153],"augmentation":[155],"practical":[158],"way":[159],"make":[161],"LLMs":[162],"less":[163],"without":[168],"changing":[169],"base":[171],"model.":[172]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-12T00:00:00"}
