{"id":"https://openalex.org/W7125250991","doi":"https://doi.org/10.48550/arxiv.2601.11581","title":"Enhancing the QA Model through a Multi-domain Debiasing Framework","display_name":"Enhancing the QA Model through a Multi-domain Debiasing Framework","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7125250991","doi":"https://doi.org/10.48550/arxiv.2601.11581"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.11581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11581","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":null,"license_id":null,"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.2601.11581","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123527865","display_name":"Yuefeng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuefeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5057627541","display_name":"Changjae Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, ChangJae","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":0.7587000131607056,"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":0.7587000131607056,"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":0.0689999982714653,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0430000014603138,"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/debiasing","display_name":"Debiasing","score":0.9927999973297119},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8197000026702881},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5760999917984009},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42309999465942383},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4203999936580658},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4065999984741211},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.3840000033378601},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.34630000591278076}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9927999973297119},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8197000026702881},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7440000176429749},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5760999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5311999917030334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4505999982357025},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.423799991607666},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4203999936580658},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4065999984741211},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.3068999946117401},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C2778780117","wikidata":"https://www.wikidata.org/wiki/Q3269423","display_name":"Reading comprehension","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.11581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11581","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.11581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11581","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8353789448738098}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Question-answering":[0],"(QA)":[1],"models":[2],"have":[3],"advanced":[4],"significantly":[5],"in":[6,22,80,93],"machine":[7],"reading":[8],"comprehension":[9],"but":[10],"often":[11],"exhibit":[12],"biases":[13],"that":[14],"hinder":[15],"their":[16],"performance,":[17],"particularly":[18],"with":[19,91],"complex":[20],"queries":[21],"adversarial":[23,40,94],"conditions.":[24],"This":[25],"study":[26],"evaluates":[27],"the":[28,32,99,108],"ELECTRA-small":[29],"model":[30],"on":[31],"Stanford":[33],"Question":[34],"Answering":[35],"Dataset":[36],"(SQuAD)":[37],"v1.1":[38],"and":[39,43,54,68,84,110],"datasets":[41],"AddSent":[42],"AddOneSent.":[44],"By":[45],"identifying":[46],"errors":[47],"related":[48],"to":[49,75,106],"lexical":[50],"bias,":[51],"numerical":[52],"reasoning,":[53],"entity":[55],"recognition,":[56],"we":[57],"develop":[58],"a":[59],"multi-domain":[60],"debiasing":[61,66],"framework":[62],"incorporating":[63],"knowledge":[64],"distillation,":[65],"techniques,":[67],"domain":[69],"expansion.":[70],"Our":[71],"results":[72],"demonstrate":[73],"up":[74],"2.6":[76],"percentage":[77],"point":[78],"improvements":[79],"Exact":[81],"Match":[82],"(EM)":[83],"F1":[85],"scores":[86],"across":[87],"all":[88],"test":[89],"sets,":[90],"gains":[92],"contexts.":[95],"These":[96],"findings":[97],"highlight":[98],"potential":[100],"of":[101,112],"targeted":[102],"bias":[103],"mitigation":[104],"strategies":[105],"enhance":[107],"robustness":[109],"reliability":[111],"natural":[113],"language":[114],"understanding":[115],"systems.":[116]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-22T00:00:00"}