{"id":"https://openalex.org/W7130323398","doi":"https://doi.org/10.48448/he5s-m650","title":"Fine-grained Confidence Estimation for Spurious Correctness Detection in Large Language Models","display_name":"Fine-grained Confidence Estimation for Spurious Correctness Detection in Large Language Models","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W7130323398","doi":"https://doi.org/10.48448/he5s-m650"},"language":"en","primary_location":{"id":"doi:10.48448/he5s-m650","doi":"https://doi.org/10.48448/he5s-m650","is_oa":true,"landing_page_url":"https://doi.org/10.48448/he5s-m650","pdf_url":null,"source":{"id":"https://openalex.org/S7407053148","display_name":"Underline Science Inc.","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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Audiovisual"},"volume":null,"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48448/he5s-m650","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126280765","display_name":"Association for Computational Linguistics 2025","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Association for Computational Linguistics 2025","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134366371","display_name":"Naoya Inoue","orcid":"https://orcid.org/0000-0002-2961-6939","observed_orcids":["https://orcid.org/0000-0002-2961-6939"]},"institutions":[{"id":"https://openalex.org/I177738480","display_name":"Japan Advanced Institute of Science and Technology","ror":"https://ror.org/03frj4r98","country_code":"JP","type":"education","lineage":["https://openalex.org/I177738480"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Inoue, Naoya","raw_affiliation_strings":["Japan Advanced Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Japan Advanced Institute of Science and Technology","institution_ids":["https://openalex.org/I177738480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107403555","display_name":"Ai Ishii","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I177738480","display_name":"Japan Advanced Institute of Science and Technology","ror":"https://ror.org/03frj4r98","country_code":"JP","type":"education","lineage":["https://openalex.org/I177738480"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ishii, Ai","raw_affiliation_strings":["JAIST"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JAIST","institution_ids":["https://openalex.org/I177738480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107403556","display_name":"Satoshi Sekine","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sekine, Satoshi","raw_affiliation_strings":["NII"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NII","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100887075","display_name":"Hisami Suzuki","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suzuki, Hisami","raw_affiliation_strings":["NII"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NII","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":0.8457000255584717,"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":0.8457000255584717,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.07840000092983246,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.03759999945759773,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/confidence-estimation","display_name":"confidence estimation","score":0.7089999914169312},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9399999976158142},{"id":"https://openalex.org/keywords/reasoning-errors","display_name":"reasoning errors","score":0.5040000081062317},{"id":"https://openalex.org/keywords/multi-hop-question-answering","display_name":"multi-hop question answering","score":0.8529999852180481},{"id":"https://openalex.org/keywords/confidence-calibration","display_name":"confidence calibration","score":0.7979999780654907},{"id":"https://openalex.org/keywords/error-localization","display_name":"error localization","score":0.6790000200271606}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.8083999752998352},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.7663999795913696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7138000130653381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5949000120162964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47929999232292175},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.4521999955177307},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C140529851","wikidata":"https://www.wikidata.org/wiki/Q5160083","display_name":"Confidence and prediction bands","level":3,"score":0.3840000033378601},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3481000065803528},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C81790035","wikidata":"https://www.wikidata.org/wiki/Q847158","display_name":"Jackknife resampling","level":3,"score":0.3091999888420105},{"id":"https://openalex.org/C2909755999","wikidata":"https://www.wikidata.org/wiki/Q4751126","display_name":"Low Confidence","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C51110983","wikidata":"https://www.wikidata.org/wiki/Q16503490","display_name":"Overconfidence effect","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48448/he5s-m650","doi":"https://doi.org/10.48448/he5s-m650","is_oa":true,"landing_page_url":"https://doi.org/10.48448/he5s-m650","pdf_url":null,"source":{"id":"https://openalex.org/S7407053148","display_name":"Underline Science Inc.","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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Audiovisual"}],"best_oa_location":{"id":"doi:10.48448/he5s-m650","doi":"https://doi.org/10.48448/he5s-m650","is_oa":true,"landing_page_url":"https://doi.org/10.48448/he5s-m650","pdf_url":null,"source":{"id":"https://openalex.org/S7407053148","display_name":"Underline Science Inc.","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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Audiovisual"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7161740095","https://openalex.org/W7134814024","https://openalex.org/W7106836370","https://openalex.org/W4404355734","https://openalex.org/W4406603696","https://openalex.org/W7127454394","https://openalex.org/W4405431306","https://openalex.org/W4407690020","https://openalex.org/W7077674820","https://openalex.org/W7170385212"],"abstract_inverted_index":{"In":[0],"the":[1,51,165],"deployment":[2],"of":[3,24,50,76,98,167,176],"Large":[4],"Language":[5],"Models":[6],"(LLMs),":[7],"``spurious":[8],"correctness''---where":[9],"answers":[10,150],"are":[11],"correct":[12,149],"but":[13],"reasoning":[14,38,52,71],"contains":[15,105],"errors---poses":[16],"a":[17,57,171],"critical":[18],"risk":[19],"by":[20,183],"creating":[21],"an":[22],"illusion":[23],"reliability.":[25],"While":[26],"prior":[27],"work":[28],"on":[29,34,109,154],"LLM":[30],"confidence":[31,59,64,92,141,177,181],"estimation":[32,60],"focuses":[33],"answer-level":[35],"or":[36],"entire":[37],"path":[39],"confidence,":[40],"these":[41],"coarse-grained":[42],"approaches":[43],"fail":[44],"to":[45,146,152,185,198],"identify":[46],"which":[47],"specific":[48],"parts":[49],"contain":[53],"errors.":[54,77,107],"We":[55],"propose":[56],"fine-grained":[58],"framework":[61],"that":[62,132],"computes":[63],"scores":[65,93,178],"for":[66,139],"individual":[67],"evidence":[68,85,104,140],"triplets":[69],"within":[70],"chains,":[72],"enabling":[73],"precise":[74],"localization":[75],"Using":[78],"carefully":[79],"designed":[80],"prompts,":[81],"we":[82,130],"generate":[83],"answers,":[84],"in":[86],"triplet":[87],"format,":[88],"and":[89,112,127,142,194],"their":[90],"respective":[91],"simultaneously,":[94],"allowing":[95],"automatic":[96],"detection":[97],"spurious":[99,148],"correctness":[100],"patterns":[101],"where":[102],"partial":[103],"factual":[106],"Evaluated":[108],"both":[110],"Japanese":[111],"English":[113],"multi-hop":[114],"QA":[115],"benchmarks":[116],"across":[117,162],"multiple":[118],"models":[119],"from":[120],"three":[121],"model":[122,192],"families":[123],"representing":[124],"different":[125],"architectures":[126],"training":[128],"approaches,":[129],"show":[131],"our":[133,155,168],"approach":[134,189],"exhibits":[135],"superior":[136],"calibration":[137,182],"performance":[138],"demonstrates":[143],"effective":[144],"ability":[145],"detect":[147],"(up":[151],"0.84":[153],"primary":[156],"discrimination":[157],"metric).":[158],"The":[159],"consistent":[160],"improvements":[161],"languages":[163],"demonstrate":[164],"generalizability":[166],"method.":[169],"As":[170],"secondary":[172],"benefit,":[173],"joint":[174],"generation":[175],"improves":[179],"answer":[180],"up":[184],"43\\%.":[186],"This":[187],"prompt-based":[188],"requires":[190],"no":[191],"retraining":[193],"is":[195],"immediately":[196],"applicable":[197],"existing":[199],"LLMs.":[200]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-19T00:00:00"}