{"id":"https://openalex.org/W4416251115","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229352","title":"Enhancing Vector Data Quality through Negative Learning for Retrieval-augmented Large Models","display_name":"Enhancing Vector Data Quality through Negative Learning for Retrieval-augmented Large Models","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251115","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229352"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229352","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229352","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091377749","display_name":"Limei Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Limei Yao","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101650067","display_name":"Kexin Ma","orcid":"https://orcid.org/0000-0002-9930-0104"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kexin Ma","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102723856","display_name":"Ruochun Jin","orcid":"https://orcid.org/0000-0001-6217-4223"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruochun Jin","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111259630","display_name":"Haoqi Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoqi Zheng","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070785308","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-8001-3199"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Wang","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.489300012588501,"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":0.489300012588501,"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/T11719","display_name":"Data Quality and Management","score":0.17059999704360962,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.07000000029802322,"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/matching","display_name":"Matching (statistics)","score":0.6011000275611877},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5760999917984009},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5252000093460083},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5241000056266785},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4447000026702881},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4074000120162964},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.34130001068115234},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.301800012588501},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.2924000024795532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7135000228881836},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6011000275611877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5976999998092651},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5806000232696533},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5760999917984009},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5252000093460083},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5241000056266785},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4074000120162964},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.34130001068115234},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26980000734329224},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C46355384","wikidata":"https://www.wikidata.org/wiki/Q726686","display_name":"Compromise","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C2988494973","wikidata":"https://www.wikidata.org/wiki/Q179448","display_name":"Noise immunity","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229352","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229352","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2150166382","https://openalex.org/W2159100004","https://openalex.org/W2169940602","https://openalex.org/W2171332293","https://openalex.org/W2425348221","https://openalex.org/W2437494956","https://openalex.org/W2912924812","https://openalex.org/W2963339397","https://openalex.org/W4283329289","https://openalex.org/W4385565351","https://openalex.org/W4385570777","https://openalex.org/W4385571124","https://openalex.org/W4389520468","https://openalex.org/W4391376033","https://openalex.org/W4393152682","https://openalex.org/W4402667082","https://openalex.org/W4402669713","https://openalex.org/W4402670727","https://openalex.org/W4402671700","https://openalex.org/W4404534210","https://openalex.org/W4404782274","https://openalex.org/W4404782899","https://openalex.org/W4404783040","https://openalex.org/W4411119345","https://openalex.org/W4415124086"],"related_works":[],"abstract_inverted_index":{"Retrieval-augmented":[0],"Large":[1],"Models":[2],"(RALMs)":[3],"have":[4],"emerged":[5],"as":[6,60],"a":[7,50,83,144],"promising":[8],"paradigm":[9],"to":[10,66,103,130],"enhance":[11,136],"large":[12],"language":[13],"models":[14],"(LLMs)":[15],"by":[16,88],"integrating":[17],"external":[18],"knowledge.":[19],"However,":[20],"the":[21,35,68,76,132],"inherent":[22],"complexity":[23],"of":[24,71],"vector-based":[25],"retrieval":[26,85,106],"often":[27],"introduces":[28],"noise":[29],"and":[30,116,126,135],"inaccuracies":[31],"that":[32],"can":[33,58],"compromise":[34],"model\u2019s":[36],"performance.":[37],"While":[38],"existing":[39],"approaches":[40],"primarily":[41],"focus":[42],"on":[43],"filtering":[44],"retrieved":[45],"contexts,":[46],"our":[47,99],"study":[48],"reveals":[49],"novel":[51],"perspective:":[52],"seemingly":[53],"irrelevant":[54],"or":[55],"contradictory":[56],"knowledge":[57,137],"serve":[59],"valuable":[61],"learning":[62,133,160],"signals":[63],"for":[64],"LLMs":[65,142],"improve":[67],"data":[69],"quality":[70],"vector":[72],"database.":[73],"We":[74],"propose":[75],"NDIC":[77],"(Negatives":[78],"Driven":[79],"Index":[80],"Correction)":[81],"framework,":[82],"context-aware":[84],"method":[86],"inspired":[87],"relational":[89],"database":[90],"theory.":[91],"By":[92],"introducing":[93],"Contexts":[94],"Clear":[95],"Matching":[96],"Dependence":[97],"(CCMDs),":[98],"approach":[100],"enables":[101],"LLM":[102],"precisely":[104],"categorize":[105],"contexts":[107,129],"into":[108,158],"three":[109],"distinct":[110],"types:":[111],"positively":[112,125],"matched,":[113,115],"negatively":[114,127],"unclear.":[117],"Unlike":[118],"traditional":[119],"methods,":[120],"we":[121],"strategically":[122],"utilize":[123],"both":[124],"matched":[128],"refine":[131],"process":[134],"utilization.":[138],"Experiments":[139],"across":[140],"diverse":[141],"demonstrate":[143],"6.17%":[145],"improvement":[146],"in":[147,149],"accuracy":[148],"challenging":[150],"question-answering":[151],"tasks,":[152],"effectively":[153],"transforming":[154],"potentially":[155],"noisy":[156],"retrievals":[157],"structured":[159],"opportunities.":[161]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}
