{"id":"https://openalex.org/W4401863316","doi":"https://doi.org/10.1145/3637528.3671444","title":"DARE to Diversify: DAta Driven and Diverse LLM REd Teaming","display_name":"DARE to Diversify: DAta Driven and Diverse LLM REd Teaming","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863316","doi":"https://doi.org/10.1145/3637528.3671444"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671444","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3637528.3671444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5064475546","display_name":"Manish Nagireddy","orcid":"https://orcid.org/0000-0001-9245-2546"},"institutions":[{"id":"https://openalex.org/I4210087032","display_name":"Cambridge Scientific (United States)","ror":"https://ror.org/001s4dh65","country_code":"US","type":"company","lineage":["https://openalex.org/I4210087032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manish Nagireddy","raw_affiliation_strings":["IBM Research, Cambridge, Massachusetts, USA"],"raw_orcid":"https://orcid.org/0000-0001-9245-2546","affiliations":[{"raw_affiliation_string":"IBM Research, Cambridge, Massachusetts, USA","institution_ids":["https://openalex.org/I4210087032"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070099634","display_name":"Bernat Guill\u00e9n Pegueroles","orcid":"https://orcid.org/0000-0002-5781-1918"},"institutions":[{"id":"https://openalex.org/I4210100430","display_name":"Google (Switzerland)","ror":"https://ror.org/014f9c269","country_code":"CH","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210100430","https://openalex.org/I4210128969"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Bernat Guill\u00e9n Pegueroles","raw_affiliation_strings":["Google, Zurich, CH"],"raw_orcid":"https://orcid.org/0000-0002-5781-1918","affiliations":[{"raw_affiliation_string":"Google, Zurich, CH","institution_ids":["https://openalex.org/I4210100430"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073341059","display_name":"Ioana Baldini","orcid":"https://orcid.org/0000-0002-8257-9866"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ioana Baldini","raw_affiliation_strings":["IBM Research, Yorktown Heights, New York, USA"],"raw_orcid":"https://orcid.org/0000-0002-8257-9866","affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, New York, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0668,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78309467,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6420","last_page":"6421"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9316999912261963,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9316999912261963,"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/T11986","display_name":"Scientific Computing and Data Management","score":0.9286999702453613,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5036606192588806}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5036606192588806}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671444","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3637528.3671444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":5,"referenced_works":["https://openalex.org/W4253763531","https://openalex.org/W4285242720","https://openalex.org/W4389523893","https://openalex.org/W4402671039","https://openalex.org/W4402671155"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"have":[4],"been":[5],"rapidly":[6],"adopted,":[7],"as":[8,27,55],"showcased":[9],"by":[10,20,49,120,142],"ChatGPT's":[11],"overnight":[12],"popularity,":[13],"and":[14,30,42,68,87,113,166,182,204,215,234],"are":[15,76,93,102,117,217],"integrated":[16],"in":[17,79,162,179,185,218,227],"products":[18],"used":[19],"millions":[21],"of":[22,37,82,109,123,128,139,146,168,194,213,236],"people":[23,115],"every":[24],"day,":[25],"such":[26,74],"search":[28],"engines":[29],"productivity":[31],"suites.":[32],"Yet":[33],"the":[34,80,83,110,114,121,137,144,147,160,164,169,177,190,201,210,224],"societal":[35],"impact":[36],"LLMs,":[38,73],"encompassing":[39],"both":[40,163,200],"benefits":[41],"harms,":[43],"is":[44,53,97,131],"not":[45],"well":[46],"understood.":[47],"Inspired":[48],"cybersecurity":[50],"practices,":[51,151],"red-teaming":[52,72,84,100,141,150,187,228],"emerging":[54],"a":[56,180,219],"technique":[57],"to":[58,105,155,175,188,222],"uncover":[59],"model":[60,196,205],"vulnerabilities.":[61],"Despite":[62],"increasing":[63],"attention":[64],"from":[65,152,239],"industry,":[66],"academia,":[67],"government":[69],"centered":[70],"around":[71],"efforts":[75,101],"still":[77],"limited":[78],"diversity":[81],"focus,":[85],"approaches":[86],"participants.":[88],"Importantly,":[89],"given":[90,230],"that":[91,99,209],"LLMs":[92],"becoming":[94],"ubiquitous,":[95],"it":[96],"imperative":[98],"scaled":[103],"out":[104],"include":[106],"large":[107],"segments":[108],"research,":[111],"practitioners":[112,216],"whom":[116],"directly":[118],"affected":[119],"deployment":[122],"these":[124],"systems.":[125],"The":[126],"goal":[127],"this":[129],"tutorial":[130],"two":[132],"fold.":[133],"First,":[134],"we":[135,173],"introduce":[136],"topic":[138],"LLM":[140,186],"reviewing":[143],"state":[145],"art":[148],"for":[149],"participatory":[153],"events":[154],"automatic":[156],"AI-focused":[157],"approaches,":[158,229],"exposing":[159,195],"gaps":[161,226],"techniques":[165],"coverage":[167],"targeted":[170,202],"harms.":[171],"Second,":[172],"plan":[174],"engage":[176],"audience":[178],"hands-on":[181],"interactive":[183],"exercise":[184],"showcase":[189],"ease":[191],"(or":[192],"difficulty)":[193],"vulnerabilities,":[197],"contingent":[198],"on":[199],"harm":[203],"capabilities.":[206],"We":[207],"believe":[208],"KDD":[211],"community":[212],"researchers":[214],"unique":[220],"position":[221],"address":[223],"existing":[225],"their":[231],"longstanding":[232],"research":[233],"practice":[235],"extracting":[237],"knowledge":[238],"data.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
