{"id":"https://openalex.org/W4416083222","doi":"https://doi.org/10.1007/s10994-025-06920-z","title":"LLiMe: enhancing text classifier explanations with large language models","display_name":"LLiMe: enhancing text classifier explanations with large language models","publication_year":2025,"publication_date":"2025-11-10","ids":{"openalex":"https://openalex.org/W4416083222","doi":"https://doi.org/10.1007/s10994-025-06920-z"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-025-06920-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06920-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06920-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06920-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075369704","display_name":"Fabrizio Angiulli","orcid":"https://orcid.org/0000-0002-9860-7569"},"institutions":[{"id":"https://openalex.org/I45204951","display_name":"University of Calabria","ror":"https://ror.org/02rc97e94","country_code":"IT","type":"education","lineage":["https://openalex.org/I45204951"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fabrizio Angiulli","raw_affiliation_strings":["DIMES, University of Calabria, Rende, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIMES, University of Calabria, Rende, Italy","institution_ids":["https://openalex.org/I45204951"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116041505","display_name":"Francesco De Luca","orcid":"https://orcid.org/0009-0006-3474-8692"},"institutions":[{"id":"https://openalex.org/I45204951","display_name":"University of Calabria","ror":"https://ror.org/02rc97e94","country_code":"IT","type":"education","lineage":["https://openalex.org/I45204951"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Francesco De Luca","raw_affiliation_strings":["DIMES, University of Calabria, Rende, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIMES, University of Calabria, Rende, Italy","institution_ids":["https://openalex.org/I45204951"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090380495","display_name":"Fabio Fassetti","orcid":"https://orcid.org/0000-0002-8416-906X"},"institutions":[{"id":"https://openalex.org/I45204951","display_name":"University of Calabria","ror":"https://ror.org/02rc97e94","country_code":"IT","type":"education","lineage":["https://openalex.org/I45204951"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fabio Fassetti","raw_affiliation_strings":["DIMES, University of Calabria, Rende, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIMES, University of Calabria, Rende, Italy","institution_ids":["https://openalex.org/I45204951"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036911190","display_name":"Simona Nistic\u00f2","orcid":"https://orcid.org/0000-0002-7386-2512"},"institutions":[{"id":"https://openalex.org/I45204951","display_name":"University of Calabria","ror":"https://ror.org/02rc97e94","country_code":"IT","type":"education","lineage":["https://openalex.org/I45204951"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Simona Nistic\u00f2","raw_affiliation_strings":["DIMES, University of Calabria, Rende, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIMES, University of Calabria, Rende, Italy","institution_ids":["https://openalex.org/I45204951"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5116041505"],"corresponding_institution_ids":["https://openalex.org/I45204951"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12267926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"114","issue":"12","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.7343000173568726,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.7343000173568726,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0333000011742115,"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"}},{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.027400000020861626,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6198999881744385},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5425999760627747},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5189999938011169},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.48420000076293945},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.4494999945163727},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.44670000672340393},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.41429999470710754},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4049000144004822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7748000025749207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6759999990463257},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6198999881744385},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5160999894142151},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4893999993801117},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.48420000076293945},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.3610999882221222},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C104054115","wikidata":"https://www.wikidata.org/wiki/Q216828","display_name":"Cohesion (chemistry)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C2776900844","wikidata":"https://www.wikidata.org/wiki/Q8028383","display_name":"Witness","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C84839998","wikidata":"https://www.wikidata.org/wiki/Q5249245","display_name":"Decision rule","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10994-025-06920-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06920-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06920-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10994-025-06920-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06920-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06920-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324136","display_name":"Universit\u00e0 della Calabria","ror":"https://ror.org/02rc97e94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416083222.pdf","grobid_xml":"https://content.openalex.org/works/W4416083222.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W2282821441","https://openalex.org/W2606462007","https://openalex.org/W2616247523","https://openalex.org/W2788403449","https://openalex.org/W2804393646","https://openalex.org/W2938423807","https://openalex.org/W2945295328","https://openalex.org/W2962772482","https://openalex.org/W2962843949","https://openalex.org/W2973136764","https://openalex.org/W2981731882","https://openalex.org/W2984635074","https://openalex.org/W2988157455","https://openalex.org/W2994120362","https://openalex.org/W3000120900","https://openalex.org/W3016419862","https://openalex.org/W3030109724","https://openalex.org/W3035503910","https://openalex.org/W3135868572","https://openalex.org/W3160936555","https://openalex.org/W3167451058","https://openalex.org/W3176714582","https://openalex.org/W3186197713","https://openalex.org/W3206825982","https://openalex.org/W3215144435","https://openalex.org/W4309674289","https://openalex.org/W4391986380","https://openalex.org/W4393161292","https://openalex.org/W4393863178","https://openalex.org/W4396668454","https://openalex.org/W4406844831","https://openalex.org/W4410033041","https://openalex.org/W4412877053","https://openalex.org/W4415209219","https://openalex.org/W7077155208"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"The":[1],"widespread":[2],"diffusion":[3],"of":[4,40,64,87,117,124,142,157,171,215],"text":[5,27],"black-box":[6,221],"classifiers":[7],"necessitates":[8],"explainable":[9],"AI":[10],"(XAI)":[11],"techniques":[12],"for":[13],"this":[14,51],"domain.":[15],"A":[16,175],"seminal":[17],"XAI":[18],"technique":[19],"is":[20,110,178],"Local":[21],"Interpretable":[22],"Model-agnostic":[23],"Explanations":[24],"(LIME).":[25],"For":[26],"classification,":[28],"LIME":[29,143],"maps":[30],"an":[31,47,200],"input":[32,66,173],"sentence":[33],"and":[34,203,232,251],"its":[35,125],"neighbours":[36,163],"into":[37],"a":[38,43,118,154,166,204,228],"bag":[39],"words,":[41],"using":[42],"linear":[44],"regressor":[45],"as":[46],"interpretable":[48,252],"model.":[49],"However,":[50],"strategy":[52],"has":[53],"significant":[54],"limitations.":[55],"Neighbouring":[56],"sentences":[57,101],"are":[58,81,208],"constructed":[59],"solely":[60],"by":[61,138,188],"extracting":[62],"subsets":[63,80],"the":[65,74,88,107,115,122,158,172,184,197,211,220,225,244],"sentence,":[67],"which":[68],"may":[69],"fail":[70],"to":[71,84,93,113,134,152,164,180,218,236],"accurately":[72],"capture":[73,183],"local":[75,185],"decision":[76,186],"boundary.":[77],"Moreover,":[78],"these":[79,136],"not":[82],"guaranteed":[83],"be":[85],"representative":[86],"classification":[89],"classes,":[90],"potentially":[91],"leading":[92],"unbalanced":[94],"or":[95,120],"misleading":[96],"interpretability.":[97],"Additionally,":[98,199],"such":[99],"generated":[100,190],"might":[102],"lack":[103],"semantic":[104],"coherence.":[105],"Furthermore,":[106],"resulting":[108],"explanation":[109,202,231],"often":[111],"limited":[112],"confirming":[114],"relevance":[116],"term":[119],"highlighting":[121],"impact":[123],"removal,":[126],"without":[127],"providing":[128,248],"deeper":[129],"insights.":[130],"This":[131],"work":[132],"tries":[133],"overcome":[135],"limitations":[137],"proposing":[139],"LLiMean":[140],"extension":[141],"that":[144,170],"exploits":[145],"advances":[146],"in":[147,196,247],"Large":[148],"Language":[149],"Models":[150],"(LLMs)":[151],"perform":[153],"classifier-driven":[155],"generation":[156,176,206],"neighbourhood.":[159],"Our":[160],"approach":[161,226],"allows":[162],"employ":[165],"vocabulary":[167],"larger":[168],"than":[169],"text.":[174],"procedure":[177,207],"introduced":[179],"more":[181],"effectively":[182],"boundary":[187],"ensuring":[189],"samples":[191],"span":[192],"all":[193],"classes":[194],"involved":[195],"classification.":[198],"LLM-driven":[201],"counterfactual":[205],"presented,":[209],"returning":[210],"most":[212],"relevant":[213],"set":[214],"editing":[216],"operations":[217],"influence":[219],"predictor\u2019s":[222],"decision.":[223],"Thus,":[224],"provides":[227],"richer,":[229],"easier-to-interpret":[230],"high-quality":[233],"counterfactuals":[234],"compared":[235],"standard":[237],"LIME.":[238],"Experiments":[239],"on":[240],"real":[241],"datasets":[242],"witness":[243],"technique\u2019s":[245],"effectiveness":[246],"suitable,":[249],"relevant,":[250],"explanations.":[253]},"counts_by_year":[],"updated_date":"2026-07-26T05:49:09.470579","created_date":"2025-11-11T00:00:00"}