{"id":"https://openalex.org/W7164057237","doi":"https://doi.org/10.48550/arxiv.2606.08589","title":"Detection and Interpretability Analysis of Quotation Errors by Large Language Models","display_name":"Detection and Interpretability Analysis of Quotation Errors by Large Language Models","publication_year":2026,"publication_date":"2026-06-07","ids":{"openalex":"https://openalex.org/W7164057237","doi":"https://doi.org/10.48550/arxiv.2606.08589"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08589","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08589","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.08589","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137855959","display_name":"Bei Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Bei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138269102","display_name":"Yingyi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137886879","display_name":"Shenghao Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Shenghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138214892","display_name":"Chengzhi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chengzhi","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":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/T10028","display_name":"Topic Modeling","score":0.1307000070810318,"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":0.1307000070810318,"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/T10102","display_name":"scientometrics and bibliometrics research","score":0.05640000104904175,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.04960000142455101,"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/interpretability","display_name":"Interpretability","score":0.9767000079154968},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6531999707221985},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5153999924659729},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.3898000121116638},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3808000087738037},{"id":"https://openalex.org/keywords/originality","display_name":"Originality","score":0.3328000009059906}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9767000079154968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7258999943733215},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6531999707221985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5587999820709229},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5153999924659729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4830999970436096},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4767000079154968},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3407000005245209},{"id":"https://openalex.org/C2776950860","wikidata":"https://www.wikidata.org/wiki/Q2914681","display_name":"Originality","level":3,"score":0.3328000009059906},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.26460000872612}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08589","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08589","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08589","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08589","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":"Preprint"},"sustainable_development_goals":[{"score":0.40760016441345215,"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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Purpose":[0],"-":[1,91,173,208],"Quotation":[2],"error":[3,58,70],"refers":[4],"to":[5,18,120,162,219],"the":[6,28,32,42,47,62,82,108,115,129,137,159,168,181,188,195,200,204,220,234],"inconsistency":[7],"between":[8],"cited":[9,130],"information":[10],"and":[11,40,44,135,228],"its":[12],"original":[13,29],"source.":[14],"This":[15],"phenomenon":[16],"leads":[17],"a":[19,93],"series":[20],"of":[21,27,37,46,68,84,87,110,128,148,222,225],"negative":[22],"impacts,":[23],"such":[24,142],"as":[25],"misinterpretation":[26],"research,":[30],"undermining":[31],"academic":[33,49,63],"community's":[34],"collective":[35],"understanding":[36],"relevant":[38],"issues,":[39],"weakening":[41],"accuracy":[43],"fairness":[45],"citation-based":[48],"evaluation":[50],"system.":[51],"Existing":[52],"studies":[53],"have":[54],"shown":[55],"that":[56],"quotation":[57,69,88,122,185,226],"is":[59,71,217,231],"prevalent":[60],"in":[61,183],"community;":[64],"moreover,":[65],"manual":[66],"verification":[67],"not":[72],"only":[73],"labor-intensive":[74],"but":[75],"also":[76],"inefficient.":[77],"Therefore,":[78],"this":[79,99,155],"paper":[80,100,156],"proposes":[81],"task":[83,221],"'automated":[85],"detection":[86,102,224],"errors'.":[89],"Methodology":[90],"Adopting":[92],"large":[94,213],"language":[95,214],"model":[96],"(LLM)-based":[97],"approach,":[98],"improves":[101],"performance":[103,182],"from":[104],"two":[105],"aspects":[106],"on":[107,153,167,198,233],"basis":[109],"existing":[111],"research:":[112],"first,":[113],"employ":[114],"fine-tuning":[116,175,210],"approach":[117,176,196,211],"for":[118,140,177,191,212],"LLMs":[119,178],"detect":[121],"errors;":[123],"second,":[124],"incorporating":[125,192],"full-text":[126,149,193],"data":[127],"literature":[131],"into":[132],"dataset":[133],"construction,":[134],"exploring":[136],"optimal":[138],"scheme":[139],"building":[141],"datasets":[143],"by":[144],"comparing":[145],"three":[146],"types":[147],"integration":[150],"methods.":[151],"Based":[152],"this,":[154],"further":[157],"uses":[158],"TokenSHAP":[160],"tool":[161],"conduct":[163],"interpretability":[164,229],"experimental":[165],"analysis":[166,230],"model's":[169,235],"prediction":[170],"results.":[171,237],"Findings":[172],"The":[174,209],"has":[179],"improved":[180],"detecting":[184],"errors.":[186],"Among":[187],"different":[189],"methods":[190],"information,":[194],"based":[197],"using":[199],"source":[201],"abstract":[202],"yielded":[203],"best":[205],"performance.":[206],"Originality":[207],"models":[215],"(LLMs)":[216],"applied":[218],"automated":[223],"errors,":[227],"conducted":[232],"output":[236]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}