{"id":"https://openalex.org/W4391113927","doi":"https://doi.org/10.1109/bigdata59044.2023.10386520","title":"A Case Study on ChatGPT Question Generation","display_name":"A Case Study on ChatGPT Question Generation","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391113927","doi":"https://doi.org/10.1109/bigdata59044.2023.10386520"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","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":null,"display_name":"Winston Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Winston Chan","raw_affiliation_strings":["York University,Dept. of Electrical Engineering and Computer Science,Toronto,Canada","Dept. of Electrical Engineering and Computer Science, York University, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University,Dept. of Electrical Engineering and Computer Science,Toronto,Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"Dept. of Electrical Engineering and Computer Science, York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009394818","display_name":"Aijun An","orcid":"https://orcid.org/0000-0003-1765-5751"},"institutions":[{"id":"https://openalex.org/I39470171","display_name":"Ontario Tech University","ror":"https://ror.org/016zre027","country_code":"CA","type":"education","lineage":["https://openalex.org/I39470171"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Aijun An","raw_affiliation_strings":["Ontario Tech University,Faculty of Science,Oshawa,Ontario","Faculty of Science, Ontario Tech University, Oshawa, Ontario"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ontario Tech University,Faculty of Science,Oshawa,Ontario","institution_ids":["https://openalex.org/I39470171"]},{"raw_affiliation_string":"Faculty of Science, Ontario Tech University, Oshawa, Ontario","institution_ids":["https://openalex.org/I39470171"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014081539","display_name":"Heidar Davoudi","orcid":"https://orcid.org/0000-0002-9603-9625"},"institutions":[{"id":"https://openalex.org/I39470171","display_name":"Ontario Tech University","ror":"https://ror.org/016zre027","country_code":"CA","type":"education","lineage":["https://openalex.org/I39470171"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Heidar Davoudi","raw_affiliation_strings":["Ontario Tech University,Faculty of Science,Oshawa,Ontario","Faculty of Science, Ontario Tech University, Oshawa, Ontario"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ontario Tech University,Faculty of Science,Oshawa,Ontario","institution_ids":["https://openalex.org/I39470171"]},{"raw_affiliation_string":"Faculty of Science, Ontario Tech University, Oshawa, Ontario","institution_ids":["https://openalex.org/I39470171"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4684,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.86212107,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1647","last_page":"1656"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9923999905586243,"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":0.9923999905586243,"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/T13751","display_name":"Diverse Approaches in Healthcare and Education Studies","score":0.9452999830245972,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9090999960899353,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social 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":0.5628383159637451}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5628383159637451}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2249280543","https://openalex.org/W2807132464","https://openalex.org/W2810503719","https://openalex.org/W2945260553","https://openalex.org/W2963748441","https://openalex.org/W3007759824","https://openalex.org/W3034999214","https://openalex.org/W3035359363","https://openalex.org/W3049467292","https://openalex.org/W3100439847","https://openalex.org/W3128371468","https://openalex.org/W3164108198","https://openalex.org/W3174099131","https://openalex.org/W3190723904","https://openalex.org/W4288089799","https://openalex.org/W4292779060","https://openalex.org/W4297793486","https://openalex.org/W4385571244","https://openalex.org/W4385572863","https://openalex.org/W4386044041","https://openalex.org/W4386566749","https://openalex.org/W4389519239","https://openalex.org/W4391681217","https://openalex.org/W4392645476","https://openalex.org/W4402288698","https://openalex.org/W6622762108","https://openalex.org/W6762122294","https://openalex.org/W6769311223","https://openalex.org/W6769627184","https://openalex.org/W6773829392","https://openalex.org/W6778883912","https://openalex.org/W6781813606","https://openalex.org/W6843476279","https://openalex.org/W6852613093","https://openalex.org/W6855193689","https://openalex.org/W6855999359","https://openalex.org/W6856036229"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,124],"advent":[1],"of":[2,8,21,65,79,138,153,162,171],"transformers":[3],"and":[4,32,37,55,120],"the":[5,19,63,77,103,113,118,139,154,160,169],"subsequent":[6],"development":[7],"Large":[9],"Language":[10,23],"Models":[11],"(LLMs)":[12],"based":[13],"on":[14,76,117],"these":[15],"technologies":[16],"has":[17,45],"revolutionized":[18],"field":[20],"Natural":[22],"Processing":[24],"(NLP).":[25],"These":[26],"models":[27],"are":[28],"able":[29,130],"to":[30,131,164,196],"understand":[31],"generate":[33],"coherent":[34],"natural":[35],"language":[36],"hold":[38],"conversations":[39],"with":[40,112,133],"humans":[41],"continuously.":[42],"Meanwhile,":[43],"ChatGPT":[44,96,128,149,163,172,186],"become":[46],"famous":[47],"among":[48],"many":[49,72],"LLMs":[50],"for":[51,100,173],"its":[52,179],"general-purpose":[53],"characteristics":[54],"versatility.":[56],"With":[57],"that":[58,88,127,145,185],"in":[59,71,178],"mind,":[60],"we":[61,143,146,157],"investigate":[62,159],"capabilities":[64],"ChatGPT,":[66],"which":[67],"is":[68,129,176],"very":[69],"successful":[70],"downstream":[73],"NLP":[74],"tasks":[75],"task":[78],"Question":[80],"Generation":[81],"(QG).":[82],"In":[83],"particular,":[84],"our":[85,92,182],"experiments":[86],"show":[87,126],"appropriate":[89,98],"context":[90],"through":[91,150],"designed":[93],"prompts":[94],"makes":[95],"an":[97],"tool":[99],"accurately":[101],"performing":[102],"QG":[104,166,192],"task.":[105],"We":[106],"compare":[107],"ChatGPT\u2019s":[108],"question":[109],"generation":[110],"results":[111,125,183],"state-of-the-art":[114],"models,":[115],"particularly":[116],"SQuAD":[119],"car":[121],"manual":[122],"datasets.":[123],"compete":[132],"or":[134],"even":[135],"outperform":[136],"some":[137],"baseline":[140],"models.":[141,167],"Furthermore,":[142],"illustrate":[144],"may":[147],"improve":[148],"additional":[151],"fine-tuning":[152],"prompts.":[155],"Finally,":[156],"also":[158],"use":[161,170],"evaluate":[165],"While":[168],"such":[174],"purposes":[175],"still":[177],"early":[180],"stages,":[181],"demonstrate":[184],"can":[187],"potentially":[188],"be":[189],"a":[190],"strong":[191],"accuracy":[193],"evaluator":[194],"comparable":[195],"human":[197],"evaluators.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}