{"id":"https://openalex.org/W7166831532","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1855","title":"Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution","display_name":"Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166831532","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1855"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.1855","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1855","pdf_url":"https://aclanthology.org/2026.findings-acl.1855.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.1855.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139843985","display_name":"Xingyu Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xingyu Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139826379","display_name":"Yumeng Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yumeng Ma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139807182","display_name":"Xiang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139793476","display_name":"Shengli Gan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shengli Gan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139823600","display_name":"Guiying Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guiying Deng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139764049","display_name":"Yang Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Wen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139778891","display_name":"Yanbing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanbing Liu","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":0.82131078,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"37252","last_page":"37265"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.33559998869895935,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.33559998869895935,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.23510000109672546,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.07360000163316727,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3774000108242035},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.3425000011920929},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.2694999873638153},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.24330000579357147},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.24240000545978546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5027999877929688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38929998874664307},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3327000141143799},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.25619998574256897},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.24330000579357147},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.24240000545978546},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.22869999706745148}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.1855","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1855","pdf_url":"https://aclanthology.org/2026.findings-acl.1855.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.1855","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1855","pdf_url":"https://aclanthology.org/2026.findings-acl.1855.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/G4444696033","display_name":null,"funder_award_id":"62201112","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8550482563","display_name":null,"funder_award_id":"62272074","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/F4320324805","display_name":"Chongqing Municipal Education Commission","ror":"https://ror.org/031nm5713"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166831532.pdf","grobid_xml":"https://content.openalex.org/works/W7166831532.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"widespread":[1],"application":[2],"of":[3,48,57,88,155],"Large":[4],"Language":[5],"Models":[6],"(LLMs)":[7],"has":[8],"made":[9],"Machine-Generated":[10],"Text":[11],"(MGT)":[12],"detection":[13,22],"increasingly":[14],"important":[15],"in":[16,32],"cyberspace":[17],"security":[18],"and":[19,41,77,119,165],"governance.The":[20],"existing":[21],"paradigms":[23],"mainly":[24],"focus":[25],"on":[26,96,99,157],"statistical":[27],"likelihood":[28],"or":[29],"deep":[30],"embeddings.However,":[31],"complex":[33],"applications":[34],"such":[35],"as":[36],"short":[37],"texts,":[38],"derivative":[39],"works,":[40],"cross-domain":[42],"content,":[43],"the":[44,55,85,100,158],"discriminative":[45],"capabilities":[46],"fragility":[47],"these":[49,69],"conventional":[50],"methods":[51],"increases":[52],"significantly":[53],"with":[54,134],"development":[56],"LLMs.Conversely,":[58],"our":[59,145],"research":[60],"reveals":[61],"that":[62,111,144],"LLMs":[63],"exhibit":[64],"inherent":[65],"style":[66],"inertia.To":[67],"address":[68],"limitations,":[70],"this":[71],"study":[72],"attempts":[73],"to":[74,128],"synergize":[75],"stylometrics":[76],"semantics":[78],"for":[79],"identifying":[80],"MGT.This":[81],"approach":[82],"draws":[83],"from":[84],"forensic":[86],"perspective":[87],"experts":[89],"who":[90],"detect":[91],"human":[92],"imitation":[93],"by":[94],"focusing":[95],"stylistic":[97,114,132],"nuances.Based":[98],"above":[101],"inspiration,":[102],"we":[103],"propose":[104],"Stylometric-Semantic":[105],"LLM":[106,141],"Attribution":[107],"(SSLA),":[108],"a":[109,123,152],"framework":[110],"extracts":[112],"model-specific":[113],"fingerprints":[115],"across":[116,139],"lexical,":[117],"syntactic,":[118],"structural":[120],"dimensions.SSLA":[121],"employs":[122],"dual-path":[124],"attention":[125],"fusion":[126],"architecture":[127],"dynamically":[129],"integrate":[130],"explicit":[131],"signals":[133],"implicit":[135],"semantic":[136],"encodings.Extensive":[137],"experiments":[138],"six":[140],"families":[142],"demonstrate":[143],"method":[146],"achieves":[147,151],"state-ofthe-art":[148],"performance.Notably,":[149],"SSLA":[150],"Macro-F1":[153],"score":[154],"95.6%":[156],"challenging":[159],"Wikipedia":[160],"dataset,":[161],"demonstrating":[162],"exceptional":[163],"robustness":[164],"surpassing":[166],"state-of-the-art":[167],"baselines":[168],"like":[169],"OTBDetector.":[170]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}