{"id":"https://openalex.org/W3154575616","doi":"https://doi.org/10.18653/v1/2021.emnlp-main.522","title":"Editing Factual Knowledge in Language Models","display_name":"Editing Factual Knowledge in Language Models","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3154575616","doi":"https://doi.org/10.18653/v1/2021.emnlp-main.522","mag":"3154575616"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2021.emnlp-main.522","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2021.emnlp-main.522","pdf_url":"https://aclanthology.org/2021.emnlp-main.522.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2021.emnlp-main.522.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075219217","display_name":"Nicola De Cao","orcid":"https://orcid.org/0000-0003-2121-9485"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB","NL"],"is_corresponding":false,"raw_author_name":"Nicola De Cao","raw_affiliation_strings":["University of Amsterdam,","University of Edinburgh","University of Amsterdam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam,","institution_ids":["https://openalex.org/I887064364"]},{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074142241","display_name":"Wilker Aziz","orcid":"https://orcid.org/0000-0002-2093-3866"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Wilker Aziz","raw_affiliation_strings":["University of Amsterdam,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam,","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086717154","display_name":"Ivan Titov","orcid":"https://orcid.org/0000-0002-2583-1893"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB","NL"],"is_corresponding":false,"raw_author_name":"Ivan Titov","raw_affiliation_strings":["University of Amsterdam,","University of Edinburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam,","institution_ids":["https://openalex.org/I887064364"]},{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6491","last_page":"6506"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9983999729156494,"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":0.9929999709129333,"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/computer-science","display_name":"Computer science","score":0.8632384538650513},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7506300210952759},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6598280668258667},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6394729018211365},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6055245399475098},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.585943341255188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5836492776870728},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5405503511428833},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4506511688232422},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.44171440601348877},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4390409588813782},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4253149926662445},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.4222261309623718},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.23294347524642944},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.11160629987716675}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8632384538650513},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7506300210952759},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6598280668258667},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6394729018211365},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6055245399475098},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.585943341255188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5836492776870728},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5405503511428833},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4506511688232422},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.44171440601348877},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4390409588813782},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4253149926662445},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.4222261309623718},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.23294347524642944},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.11160629987716675},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.18653/v1/2021.emnlp-main.522","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2021.emnlp-main.522","pdf_url":"https://aclanthology.org/2021.emnlp-main.522.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/42c3377e-fa49-45d7-a3ba-4372c1fb0492","is_oa":true,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/editing-factual-knowledge-in-language-models(42c3377e-fa49-45d7-a3ba-4372c1fb0492).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"De Cao, N, Aziz, W & Titov, I 2021, Editing Factual Knowledge in Language Models. in M-C Moens, X Huang, L Specia & S W Sih (eds), 2021 Conference on Empirical Methods in Natural Language Processing : EMNLP 2021 : proceedings of the conference : November 7-11, 2021. Stroudsburg, PA, pp. 6491-6506, 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual, Punta Cana, Dominican Republic, 7/11/21. https://doi.org/10.18653/v1/2021.emnlp-main.522","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:arXiv.org:2104.08164","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.08164","pdf_url":"https://arxiv.org/pdf/2104.08164","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2104.08164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2104.08164","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"},{"id":"mag:3154575616","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null},{"id":"mag:3213870215","is_oa":false,"landing_page_url":"https://aclanthology.org/2021.emnlp-main.522/","pdf_url":null,"source":{"id":"https://openalex.org/S4306418267","display_name":"Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Empirical Methods in Natural Language Processing","raw_type":null}],"best_oa_location":{"id":"doi:10.18653/v1/2021.emnlp-main.522","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2021.emnlp-main.522","pdf_url":"https://aclanthology.org/2021.emnlp-main.522.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G5226226009","display_name":"Scaling Semantic Parsing to Unrestricted Domains","funder_award_id":"639.022.518","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G6458387989","display_name":"Global Under-Resourced MEedia Translation","funder_award_id":"825299","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3154575616.pdf","grobid_xml":"https://content.openalex.org/works/W3154575616.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W196214544","https://openalex.org/W2064675550","https://openalex.org/W2095705004","https://openalex.org/W2130942839","https://openalex.org/W2183341477","https://openalex.org/W2296319761","https://openalex.org/W2420245003","https://openalex.org/W2560647685","https://openalex.org/W2604763608","https://openalex.org/W2626778328","https://openalex.org/W2906152891","https://openalex.org/W2912924812","https://openalex.org/W2922523190","https://openalex.org/W2938830017","https://openalex.org/W2962881743","https://openalex.org/W2963216553","https://openalex.org/W2963339397","https://openalex.org/W2963341956","https://openalex.org/W2963748441","https://openalex.org/W2964121744","https://openalex.org/W2964212550","https://openalex.org/W2970476646","https://openalex.org/W2970820321","https://openalex.org/W2982399380","https://openalex.org/W2996641835","https://openalex.org/W2997200074","https://openalex.org/W3017701505","https://openalex.org/W3082274269","https://openalex.org/W3098267758","https://openalex.org/W3098903812","https://openalex.org/W3102659883","https://openalex.org/W3102844651","https://openalex.org/W3106325613","https://openalex.org/W3107969673","https://openalex.org/W3118901667","https://openalex.org/W3119164154","https://openalex.org/W3121191823","https://openalex.org/W3136215575","https://openalex.org/W3139080614","https://openalex.org/W3159900299"],"related_works":["https://openalex.org/W3015770160","https://openalex.org/W2162872860","https://openalex.org/W2770653590","https://openalex.org/W2945052683","https://openalex.org/W3085769582","https://openalex.org/W3167352803","https://openalex.org/W3035091181","https://openalex.org/W2962818867","https://openalex.org/W3201662444","https://openalex.org/W3091620232","https://openalex.org/W3159526778","https://openalex.org/W2887357210","https://openalex.org/W2807535589","https://openalex.org/W2911857455","https://openalex.org/W3154482395","https://openalex.org/W2988134581","https://openalex.org/W2787381285","https://openalex.org/W3175914347","https://openalex.org/W176294486"],"abstract_inverted_index":{"The":[0],"factual":[1,206],"knowledge":[2,51],"acquired":[3],"during":[4,186],"pretraining":[5,79],"and":[6,128,138],"stored":[7],"in":[8,18,77,163,167],"the":[9,60,81,101,104,106,114,154,212,247,265],"parameters":[10],"of":[11,83,103,157,222,249,267],"Language":[12],"Models":[13],"(LMs)":[14],"can":[15,30,45,177,191],"be":[16,31,46,178,192,202,216],"useful":[17],"downstream":[19],"tasks":[20],"(e.g.,":[21,80,183],"question":[22,145],"answering":[23],"or":[24,34,56,65],"textual":[25],"inference).":[26],"However,":[27],"some":[28],"facts":[29],"incorrectly":[32],"induced":[33],"become":[35],"obsolete":[36],"over":[37],"time.":[38,119],"We":[39,120,173],"present":[40],"KNOWLEDGEEDITOR,":[41],"a":[42,90,97,132,140,151,158,164,195,219],"method":[43],"which":[44,198],"used":[47,111],"to":[48,95,112,161,201,204,215],"edit":[49],"this":[50,176],"and,":[52],"thus,":[53],"fix":[54],"'bugs'":[55],"unexpected":[57],"predictions":[58,168],"without":[59,99],"need":[61,200],"for":[62,136,144,170],"expensive":[63],"retraining":[64],"fine-tuning.":[66],"Besides":[67],"being":[68],"computationally":[69],"efficient,":[70],"KNOWLEDGEEDITOR":[71],"does":[72],"not":[73],"require":[74],"any":[75],"modifications":[76],"LM":[78],"use":[82],"meta-learning).":[84],"In":[85],"our":[86,148,189,208],"approach,":[87],"we":[88],"train":[89],"hyper-network":[91,108,190],"with":[92,124],"constrained":[93],"optimization":[94],"modify":[96],"fact":[98],"affecting":[100],"rest":[102],"knowledge;":[105,207],"trained":[107],"is":[109,226,246,264],"then":[110],"predict":[113],"weight":[115],"update":[116],"at":[117],"test":[118],"show":[121,174],"KNOWL-EDGEEDITOR's":[122],"efficacy":[123],"two":[125],"popular":[126],"architectures":[127],"knowledge-intensive":[129],"tasks:":[130],"i)":[131],"BERT":[133],"model":[134,143],"fine-tuned":[135],"fact-checking,":[137],"ii)":[139],"sequence-to-sequence":[141],"BART":[142],"answering.":[146],"With":[147],"method,":[149],"changing":[150],"prediction":[152],"on":[153,218],"specific":[155],"wording":[156],"query":[159],"tends":[160],"result":[162],"consistent":[165],"change":[166],"also":[169],"its":[171],"paraphrases.":[172],"that":[175,211],"further":[179],"encouraged":[180],"by":[181],"exploiting":[182],"automatically-generated)":[184],"paraphrases":[185],"training.":[187],"Interestingly,":[188],"regarded":[193],"as":[194],"'probe'":[196],"revealing":[197],"components":[199],"changed":[203],"manipulate":[205],"analysis":[209],"shows":[210],"updates":[213],"tend":[214],"concentrated":[217],"small":[220],"subset":[221],"components.":[223],"1":[224],"How":[225],"Namibia's":[227],"capital":[228,248,266],"city":[229],"called?":[230],"Semantically":[231],"equivalent":[232],"Answers":[233,251,269],"Scores":[234,252,270],"Namibia":[235,238,253,257],"Nigeria":[236,254],"Nibia":[237,255],"Tasman":[239,256],"-0.43":[240],"-0.69":[241],"-0.89":[242],"-1.08":[243],"-1.19":[244],"What":[245,263],"Namibia?":[250],"-0.32":[258],"-0.79":[259],"-0.87":[260],"-1.14":[261],"-1.16":[262],"Russia?":[268]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5}],"updated_date":"2026-07-20T07:56:41.581041","created_date":"2025-10-10T00:00:00"}
