{"id":"https://openalex.org/W7166879896","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2119","title":"C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts","display_name":"C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166879896","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2119"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.2119","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2119","pdf_url":"https://aclanthology.org/2026.findings-acl.2119.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.2119.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119812153","display_name":"Chenxi Qing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenxi Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133562592","display_name":"Junxi Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junxi Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139829446","display_name":"Zheng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133588027","display_name":"Yixiang Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yixiang Qiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109681315","display_name":"Hongyao Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongyao Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139767519","display_name":"Bin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139838076","display_name":"Hao Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139837941","display_name":"Shu-Tao Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shu-Tao Xia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86799128,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"42703","last_page":"42733"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.4828000068664551,"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":0.4828000068664551,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.0835999995470047,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.0502999983727932,"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/benchmark","display_name":"Benchmark (surveying)","score":0.4392000138759613},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.36239999532699585},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.30880001187324524},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.27129998803138733}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696000099182129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5264999866485596},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41830000281333923},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36239999532699585},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32989999651908875},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30880001187324524},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.28209999203681946},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26330000162124634}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.2119","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2119","pdf_url":"https://aclanthology.org/2026.findings-acl.2119.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.2119","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2119","pdf_url":"https://aclanthology.org/2026.findings-acl.2119.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1478944673","display_name":null,"funder_award_id":"62576122","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6595704328","display_name":null,"funder_award_id":"62301189","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G824329526","display_name":null,"funder_award_id":"62571298","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166879896.pdf","grobid_xml":"https://content.openalex.org/works/W7166879896.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"are":[5],"capable":[6],"of":[7,48],"generating":[8],"highly":[9],"fluent":[10],"textual":[11],"content.While":[12],"they":[13,19],"offer":[14],"significant":[15],"convenience":[16],"to":[17,34,88],"humans,":[18],"also":[20,84],"introduce":[21],"various":[22],"risks,":[23],"like":[24],"phishing":[25],"and":[26,41,57,91,102],"academic":[27],"dishonesty.Numerous":[28],"research":[29],"efforts":[30],"have":[31,106],"been":[32],"dedicated":[33],"developing":[35],"algorithms":[36],"for":[37],"detecting":[38],"AI-generated":[39,70],"text":[40,71],"constructing":[42],"relevant":[43],"datasets.However,":[44],"in":[45,97],"the":[46],"domain":[47,100],"Chinese":[49,68,93,109],"corpora,":[50],"challenges":[51],"remain,":[52],"including":[53],"limited":[54,107],"model":[55,98],"diversity":[56],"data":[58],"homogeneity.To":[59],"address":[60],"these":[61],"issues,":[62],"we":[63],"propose":[64],"C-ReD:":[65],"a":[66],"comprehensive":[67],"Real-prompt":[69],"Detection":[72],"benchmark.Experiments":[73],"demonstrate":[74],"that":[75,105],"C-ReD":[76],"not":[77],"only":[78],"enables":[79],"reliable":[80],"in-domain":[81],"detection":[82,110],"but":[83],"supports":[85],"strong":[86],"generalization":[87],"unseen":[89],"LLMs":[90],"external":[92],"datasets-addressing":[94],"critical":[95],"gaps":[96],"diversity,":[99],"coverage,":[101],"prompt":[103],"realism":[104],"prior":[108],"benchmarks.":[111]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}