{"id":"https://openalex.org/W7166514101","doi":"https://doi.org/10.63317/5c8w857kxgsn","title":"Benchmarking Check-Worthiness Models on LLM Generated Claims","display_name":"Benchmarking Check-Worthiness Models on LLM Generated Claims","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7166514101","doi":"https://doi.org/10.63317/5c8w857kxgsn"},"language":"en","primary_location":{"id":"doi:10.63317/5c8w857kxgsn","doi":"https://doi.org/10.63317/5c8w857kxgsn","is_oa":true,"landing_page_url":"https://doi.org/10.63317/5c8w857kxgsn","pdf_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/indor/pdf/2026.indor-1.4.pdf","source":{"id":"https://openalex.org/S7407088301","display_name":"Language Resources and Evaluation Conference (LREC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Language Resources and Evaluation Conference","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/indor/pdf/2026.indor-1.4.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119181306","display_name":"Charlie Roadhouse","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Charlie George Roadhouse","raw_affiliation_strings":["Manchester Metropolitan University Ormond Building , Lower Ormond Street , Manchester , M15 6BX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manchester Metropolitan University Ormond Building , Lower Ormond Street , Manchester , M15 6BX","institution_ids":["https://openalex.org/I11983389"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035746903","display_name":"Matthew Shardlow","orcid":"https://orcid.org/0000-0003-1129-2750","observed_orcids":["https://orcid.org/0000-0003-1129-2750"]},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Matthew Shardlow","raw_affiliation_strings":["Manchester Metropolitan University Ormond Building , Lower Ormond Street , Manchester , M15 6BX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manchester Metropolitan University Ormond Building , Lower Ormond Street , Manchester , M15 6BX","institution_ids":["https://openalex.org/I11983389"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085474120","display_name":"A W S Williams","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ashley Williams","raw_affiliation_strings":["Manchester Metropolitan University Ormond Building , Lower Ormond Street , Manchester , M15 6BX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manchester Metropolitan University Ormond Building , Lower Ormond Street , Manchester , M15 6BX","institution_ids":["https://openalex.org/I11983389"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I11983389"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43083177,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"34","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.0006000000284984708,"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/T11644","display_name":"Spam and Phishing Detection","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/fact-checking","display_name":"fact-checking","score":0.9100000262260437},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.800000011920929},{"id":"https://openalex.org/keywords/disinformation","display_name":"disinformation","score":0.8989999890327454},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"synthetic data","score":0.3449999988079071},{"id":"https://openalex.org/keywords/stylistic-categories","display_name":"stylistic categories","score":0.546999990940094},{"id":"https://openalex.org/keywords/syntactic-complexity","display_name":"syntactic complexity","score":0.7979999780654907},{"id":"https://openalex.org/keywords/adversarial-training","display_name":"adversarial training","score":0.9419999718666077},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.9049999713897705},{"id":"https://openalex.org/keywords/bert","display_name":"BERT","score":0.8489999771118164},{"id":"https://openalex.org/keywords/roberta","display_name":"RoBERTa","score":0.984000027179718}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7498000264167786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7254999876022339},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.6876000165939331},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.616100013256073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5942000150680542},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5644999742507935},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5171999931335449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49549999833106995},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.41920000314712524},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4059999883174896},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.39079999923706055},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C2779525943","wikidata":"https://www.wikidata.org/wiki/Q1187300","display_name":"Grammaticality","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.63317/5c8w857kxgsn","doi":"https://doi.org/10.63317/5c8w857kxgsn","is_oa":true,"landing_page_url":"https://doi.org/10.63317/5c8w857kxgsn","pdf_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/indor/pdf/2026.indor-1.4.pdf","source":{"id":"https://openalex.org/S7407088301","display_name":"Language Resources and Evaluation Conference (LREC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Language Resources and Evaluation Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.63317/5c8w857kxgsn","doi":"https://doi.org/10.63317/5c8w857kxgsn","is_oa":true,"landing_page_url":"https://doi.org/10.63317/5c8w857kxgsn","pdf_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/indor/pdf/2026.indor-1.4.pdf","source":{"id":"https://openalex.org/S7407088301","display_name":"Language Resources and Evaluation Conference (LREC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Language Resources and Evaluation Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166514101.pdf","grobid_xml":"https://content.openalex.org/works/W7166514101.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7130613968","https://openalex.org/W4404781625","https://openalex.org/W7118539790","https://openalex.org/W7148746641","https://openalex.org/W7197207393","https://openalex.org/W4416073435","https://openalex.org/W7200117222","https://openalex.org/W4396822170","https://openalex.org/W7123690016","https://openalex.org/W4401042272"],"abstract_inverted_index":{"The":[0],"proliferation":[1],"of":[2,15,133],"large":[3],"language":[4],"models":[5,24,44,108,119],"(LLMs)":[6],"has":[7],"significantly":[8,86],"increased":[9],"the":[10,99,131],"potential":[11],"for":[12,20],"automated":[13],"dissemination":[14],"disinformation,":[16],"necessitating":[17],"robust":[18,118],"systems":[19],"check-worthiness":[21],"detection.However,":[22],"existing":[23],"are":[25],"primarily":[26],"trained":[27],"on":[28,34,73,77,98,110,125],"human":[29],"claims,":[30,75],"leaving":[31],"their":[32,123],"performance":[33,71],"machine-generated":[35],"text":[36],"largely":[37],"unexplored.In":[38],"this":[39],"paper,":[40],"we":[41],"benchmark":[42],"encoder":[43],"(BERT":[45],"and":[46,48,64,79],"RoBERTa)":[47],"industry":[49],"accessible":[50],"tools":[51],"(ClaimBuster)":[52],"against":[53],"LLM-paraphrased":[54],"claims":[55],"across":[56],"three":[57],"stylistic":[58],"categories:":[59],"syntactic":[60,62,112],"restructuring,":[61],"complexity":[63],"lexical":[65],"informality.Our":[66],"results":[67],"indicate":[68],"a":[69],"consistent":[70],"degradation":[72],"synthetic":[74],"particularly":[76],"complex":[78],"informal":[80],"claims.We":[81],"demonstrate":[82],"that":[83,105],"adversarial":[84],"training":[85,135],"improves":[87],"model":[88],"resilience,":[89],"with":[90],"RoBERTa":[91],"achieving":[92],"F1-score":[93],"gains":[94],"up":[95],"to":[96,121,136],"+5.22":[97],"CheckIt":[100],"dataset.Finally,":[101],"SHAP":[102],"analysis":[103],"reveals":[104],"while":[106],"base":[107],"rely":[109],"narrow":[111],"heuristics":[113],"such":[114],"as":[115],"active":[116],"voice,":[117],"learn":[120],"anchor":[122],"prediction":[124],"core":[126],"factual":[127],"entities.These":[128],"findings":[129],"highlight":[130],"necessity":[132],"stylistic-aware":[134],"maintain":[137],"fact-checking":[138],"efficacy":[139],"in":[140],"an":[141],"increasingly":[142],"LLM-populated":[143],"information":[144],"landscape.":[145]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-06-30T00:00:00"}