{"id":"https://openalex.org/W7140144986","doi":"https://doi.org/10.48550/arxiv.2603.19293","title":"LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection","display_name":"LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7140144986","doi":"https://doi.org/10.48550/arxiv.2603.19293"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19293","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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.19293","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130368283","display_name":"Weilin Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhou, Weilin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130331712","display_name":"Shanwen Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Shanwen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130396232","display_name":"Enhao Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Enhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130354977","display_name":"Yurong Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Yurong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5130368283"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9940000176429749,"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":0.9940000176429749,"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":0.00039999998989515007,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inefficiency","display_name":"Inefficiency","score":0.5317999720573425},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.41019999980926514},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4047999978065491},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.37700000405311584},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.37059998512268066},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3483000099658966},{"id":"https://openalex.org/keywords/fake-news","display_name":"Fake news","score":0.3418999910354614},{"id":"https://openalex.org/keywords/reasoning-system","display_name":"Reasoning system","score":0.33629998564720154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7910000085830688},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.5317999720573425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5214999914169312},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4047999978065491},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3422999978065491},{"id":"https://openalex.org/C2779756789","wikidata":"https://www.wikidata.org/wiki/Q28549308","display_name":"Fake news","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C2780907237","wikidata":"https://www.wikidata.org/wiki/Q2986238","display_name":"Plagiarism detection","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2851000130176544},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C103057564","wikidata":"https://www.wikidata.org/wiki/Q4751139","display_name":"Analytic reasoning","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19293","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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19293","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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.44930553436279297,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"fake":[1],"news":[2],"detection":[3],"is":[4,155],"crucial":[5],"for":[6,26,67],"mitigating":[7],"societal":[8],"disinformation.":[9],"Existing":[10],"approaches":[11],"attempt":[12],"to":[13,50],"address":[14,58],"this":[15,114],"by":[16],"fusing":[17],"multimodal":[18],"features":[19],"or":[20],"leveraging":[21],"Large":[22],"Language":[23],"Models":[24],"(LLMs)":[25],"advanced":[27],"reasoning.":[28],"However,":[29],"these":[30,59],"methods":[31,150],"suffer":[32],"from":[33,87],"serious":[34],"limitations,":[35],"including":[36],"a":[37,73,84,133],"lack":[38],"of":[39,55,137],"comprehensive":[40,85,134],"multi-view":[41],"judgment":[42],"and":[43,45,90,141,151],"fusion,":[44],"prohibitive":[46],"reasoning":[47,101],"inefficiency":[48],"due":[49],"the":[51,94,119],"high":[52],"computational":[53],"costs":[54],"LLMs.":[56],"To":[57],"issues,":[60],"we":[61],"propose":[62],"\\textbf{LLM}-Guided":[63],"\\textbf{M}ulti-View":[64],"\\textbf{R}easoning":[65],"\\textbf{D}istillation":[66],"Fake":[68],"News":[69],"Detection":[70],"(":[71],"\\textbf{LLM-MRD}),":[72],"novel":[74],"teacher-student":[75],"framework.":[76],"The":[77],"Student":[78],"Multi-view":[79,96],"Reasoning":[80,97],"module":[81,98],"first":[82],"constructs":[83],"foundation":[86],"textual,":[88],"visual,":[89],"cross-modal":[91],"perspectives.":[92],"Then,":[93],"Teacher":[95],"generates":[99],"deep":[100],"chains":[102],"as":[103],"rich":[104],"supervision":[105],"signals.":[106],"Our":[107,153],"core":[108],"Calibration":[109],"Distillation":[110],"mechanism":[111],"efficiently":[112],"distills":[113],"complex":[115],"reasoning-derived":[116],"knowledge":[117],"into":[118],"efficient":[120],"student":[121],"model.":[122],"Experiments":[123],"show":[124],"LLM-MRD":[125],"significantly":[126],"outperforms":[127],"state-of-the-art":[128],"baselines.":[129],"Notably,":[130],"it":[131],"demonstrates":[132],"average":[135],"improvement":[136],"5.19\\%":[138],"in":[139,143],"ACC":[140],"6.33\\%":[142],"F1-Fake":[144],"when":[145],"evaluated":[146],"across":[147],"all":[148],"competing":[149],"datasets.":[152],"code":[154],"available":[156],"at":[157],"https://github.com/Nasuro55/LLM-MRD":[158]},"counts_by_year":[],"updated_date":"2026-03-24T06:04:31.470712","created_date":"2026-03-24T00:00:00"}
