{"id":"https://openalex.org/W7155206653","doi":"https://doi.org/10.48550/arxiv.2604.18803","title":"LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models","display_name":"LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155206653","doi":"https://doi.org/10.48550/arxiv.2604.18803"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18803","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.18803","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134261740","display_name":"Zhiyuan Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Zhiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122912392","display_name":"Weihao Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Weihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122968307","display_name":"Xinlei Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Xinlei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093503845","display_name":"Tejaswi Dhandu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dhandu, Tejaswi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063351983","display_name":"Miles Q. Li","orcid":"https://orcid.org/0000-0001-7091-3268"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Miles Q.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134334792","display_name":"Meng Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Meng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076695361","display_name":"Kuan Huang","orcid":"https://orcid.org/0000-0001-5710-1118"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Kuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133592545","display_name":"Umamaheswara Rao Tida","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tida, Umamaheswara Rao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102911920","display_name":"Bingyu Shen","orcid":"https://orcid.org/0000-0002-0792-7904"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Bingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009184228","display_name":"Daehan Kwak","orcid":"https://orcid.org/0000-0001-5614-0190"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kwak, Daehan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134315640","display_name":"Boyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Boyang","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/T11094","display_name":"Face Recognition and Perception","score":0.1662999987602234,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11094","display_name":"Face Recognition and Perception","score":0.1662999987602234,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.11150000244379044,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.09610000252723694,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/task","display_name":"Task (project management)","score":0.589900016784668},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5238000154495239},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5080999732017517},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.46869999170303345},{"id":"https://openalex.org/keywords/tone","display_name":"Tone (literature)","score":0.4650999903678894},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.45809999108314514},{"id":"https://openalex.org/keywords/directive","display_name":"Directive","score":0.44179999828338623},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.4255000054836273}],"concepts":[{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.589900016784668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.552299976348877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5292999744415283},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5080999732017517},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47279998660087585},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C2780583480","wikidata":"https://www.wikidata.org/wiki/Q1366327","display_name":"Tone (literature)","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.45809999108314514},{"id":"https://openalex.org/C2779547435","wikidata":"https://www.wikidata.org/wiki/Q1121492","display_name":"Directive","level":2,"score":0.44179999828338623},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4153999984264374},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.387800008058548},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3352000117301941},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.33009999990463257},{"id":"https://openalex.org/C2779110517","wikidata":"https://www.wikidata.org/wiki/Q1240788","display_name":"Supervisor","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3068999946117401},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18803","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.18803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18803","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://metadata.un.org/sdg/4","score":0.8154053688049316,"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":{"Vision-Language":[0],"Models":[1],"(VLMs)":[2],"are":[3],"increasingly":[4],"deployed":[5],"in":[6,149,220],"settings":[7],"where":[8],"reliable":[9],"visual":[10],"grounding":[11],"carries":[12],"operational":[13],"consequences,":[14],"yet":[15],"their":[16],"behavior":[17],"under":[18,82],"progressively":[19],"coercive":[20],"prompt":[21,218],"phrasing":[22],"remains":[23],"undercharacterized.":[24],"Existing":[25],"hallucination":[26],"benchmarks":[27],"predominantly":[28],"rely":[29],"on":[30,165],"neutral":[31],"prompts":[32,104],"and":[33,42,78,116,161,172,205,213,224],"binary":[34],"detection,":[35],"leaving":[36],"open":[37],"how":[38],"both":[39],"the":[40,43,88,114,131,145,170],"incidence":[41],"intensity":[44],"of":[45,64,147,174,191],"fabrication":[46,175],"respond":[47,216],"to":[48,217],"graded":[49],"linguistic":[50],"pressure":[51,219],"across":[52,72,209],"structurally":[53],"distinct":[54],"task":[55,74,117],"types.":[56],"We":[57,135,179],"present":[58],"Ghost-100,":[59],"a":[60,83,107,137,141,151,162,166,182],"procedurally":[61],"constructed":[62],"benchmark":[63],"800":[65,192],"synthetically":[66],"generated":[67],"images":[68,193],"spanning":[69],"eight":[70],"categories":[71],"three":[73],"families:":[75],"text-illegibility,":[76],"time-reading,":[77],"object-absence,":[79],"each":[80],"designed":[81],"negative-ground-truth":[84],"principle":[85],"that":[86,126,203,236],"guarantees":[87],"queried":[89],"target":[90],"is":[91,100,128],"absent,":[92],"illegible,":[93],"or":[94],"indeterminate":[95],"by":[96],"construction.":[97],"Every":[98],"image":[99,115],"paired":[101],"with":[102],"five":[103],"drawn":[105],"from":[106,154],"structured":[108],"5-Level":[109],"Prompt":[110],"Intensity":[111],"Framework,":[112],"holding":[113],"identity":[118],"fixed":[119],"while":[120],"varying":[121],"only":[122],"directive":[123],"force,":[124],"so":[125],"tone":[127,233],"isolated":[129],"as":[130,194],"sole":[132],"independent":[133],"variable.":[134],"adopt":[136],"dual-track":[138],"evaluation":[139],"protocol:":[140],"rule-based":[142],"H-Rate":[143,204],"measuring":[144],"proportion":[146],"responses":[148],"which":[150,187],"model":[152,210],"crosses":[153],"grounded":[155],"refusal":[156],"into":[157],"unsupported":[158],"positive":[159],"commitment,":[160],"GPT-4o-mini-judged":[163],"H-Score":[164,206],"1-5":[167],"scale":[168],"characterizing":[169],"confidence":[171],"specificity":[173],"once":[176],"it":[177],"occurs.":[178],"additionally":[180],"release":[181],"three-stage":[183],"automated":[184],"validation":[185],"workflow,":[186],"retrospectively":[188],"confirms":[189],"717":[190],"strictly":[195],"compliant.":[196],"Evaluating":[197],"nine":[198],"open-weight":[199],"VLMs,":[200],"we":[201],"find":[202],"dissociate":[207],"substantially":[208],"families,":[211],"reading-style":[212],"presence-detection":[214],"subsets":[215],"qualitatively":[221],"different":[222],"ways,":[223],"several":[225],"models":[226],"exhibit":[227],"non-monotonic":[228],"sensitivity":[229],"peaking":[230],"at":[231],"intermediate":[232],"levels:":[234],"patterns":[235],"aggregate":[237],"metrics":[238],"obscure.":[239]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-23T00:00:00"}