{"id":"https://openalex.org/W4388651113","doi":"https://doi.org/10.48550/arxiv.2311.06233","title":"Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models","display_name":"Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4388651113","doi":"https://doi.org/10.48550/arxiv.2311.06233"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2311.06233","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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2311.06233","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040083359","display_name":"Shahriar Golchin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golchin, Shahriar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047699502","display_name":"Mihai Surdeanu","orcid":"https://orcid.org/0000-0001-6956-8030"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Surdeanu, Mihai","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":1,"citation_normalized_percentile":{"value":0.787004,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":68,"max":79},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9734,"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":0.9734,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9298,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9184,"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/trait","display_name":"Trait","score":0.49887586},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.45177597}],"concepts":[{"id":"https://openalex.org/C112570922","wikidata":"https://www.wikidata.org/wiki/Q60528603","display_name":"Contamination","level":2,"score":0.82724714},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.62921387},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.61777365},{"id":"https://openalex.org/C106934330","wikidata":"https://www.wikidata.org/wiki/Q1971873","display_name":"Trait","level":2,"score":0.49887586},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45177597},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4199829},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4190507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3347043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3265803},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14699084},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2311.06233","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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2311.06233","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2311.06233","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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4249711192","https://openalex.org/W4246525302","https://openalex.org/W2952313416","https://openalex.org/W281735054","https://openalex.org/W2565462584","https://openalex.org/W2355749553","https://openalex.org/W2327028314","https://openalex.org/W2070278412","https://openalex.org/W2056851638","https://openalex.org/W1502203608"],"abstract_inverted_index":{"We":[0],"propose":[1],"the":[2,23,66,70,73,80,85,92,97,100,105,114,118,122],"Data":[3],"Contamination":[4],"Quiz":[5],"(DCQ),":[6],"a":[7,34,41],"simple":[8],"and":[9,21,39,155,169],"effective":[10],"approach":[11],"to":[12,104,130,138,151,165,178],"detect":[13],"data":[14,30,154],"contamination":[15,31,162],"in":[16,72,132],"large":[17],"language":[18],"models":[19],"(LLMs)":[20],"estimate":[22],"amount":[24],"of":[25,36,48,84],"it.":[26],"Specifically,":[27],"we":[28],"frame":[29],"detection":[32,167],"as":[33],"series":[35],"multiple-choice":[37],"questions":[38],"devise":[40],"quiz":[42],"format":[43],"wherein":[44],"three":[45],"perturbed":[46,62],"versions":[47],"each":[49],"dataset":[50],"instance":[51,116],"are":[52],"created.":[53],"These":[54],"changes":[55],"only":[56,93],"include":[57],"word-level":[58],"perturbations.":[59],"The":[60],"generated":[61],"versions,":[63],"along":[64],"with":[65,75,112,144],"original":[67,106,115,123],"instance,":[68,107],"form":[69],"options":[71],"DCQ,":[74],"an":[76,108],"extra":[77],"option":[78],"accommodating":[79],"possibility":[81],"that":[82,91,158],"none":[83],"provided":[86],"choices":[87,98],"is":[88,99],"correct.":[89],"Given":[90],"distinguishing":[94],"signal":[95],"among":[96],"exact":[101],"wording":[102],"relative":[103],"LLM,":[109],"when":[110],"tasked":[111],"identifying":[113],"from":[117],"choices,":[119],"gravitates":[120],"towards":[121],"one":[124],"if":[125],"it":[126,131],"has":[127],"been":[128],"exposed":[129],"its":[133],"pre-training":[134,153],"phase--a":[135],"trait":[136],"intrinsic":[137],"LLMs.":[139],"Tested":[140],"over":[141],"several":[142],"datasets":[143],"GPT-4/3.5,":[145],"our":[146],"findings--while":[147],"fully":[148],"lacking":[149],"access":[150],"LLMs'":[152],"internal":[156],"parameters--suggest":[157],"DCQ":[159],"uncovers":[160],"greater":[161],"levels":[163],"compared":[164],"existing":[166],"methods":[168],"proficiently":[170],"bypasses":[171],"more":[172],"safety":[173],"filters,":[174],"especially":[175],"those":[176],"set":[177],"avoid":[179],"generating":[180],"copyrighted":[181],"contents.":[182]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4388651113","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2024-12-07T05:38:17.214866","created_date":"2023-11-14"}