{"id":"https://openalex.org/W4416156928","doi":"https://doi.org/10.48550/arxiv.2511.05913","title":"NILC: Discovering New Intents with LLM-assisted Clustering","display_name":"NILC: Discovering New Intents with LLM-assisted Clustering","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4416156928","doi":"https://doi.org/10.48550/arxiv.2511.05913"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.05913","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.05913","pdf_url":"https://arxiv.org/pdf/2511.05913","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.05913","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100450683","display_name":"Hongtao Wang","orcid":"https://orcid.org/0000-0002-6050-6113","observed_orcids":["https://orcid.org/0000-0002-6050-6113"]},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wang, Hongtao","raw_affiliation_strings":["Hong Kong Baptist University Hong Kong SAR , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong Baptist University Hong Kong SAR , China","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040420455","display_name":"Renchi Yang","orcid":"https://orcid.org/0000-0002-7284-3096","observed_orcids":["https://orcid.org/0000-0002-7284-3096"]},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yang, Renchi","raw_affiliation_strings":["Hong Kong Baptist University Hong Kong SAR , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong Baptist University Hong Kong SAR , China","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043207736","display_name":"Wenqing Lin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Wenqing","raw_affiliation_strings":["China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China","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":true,"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/T12031","display_name":"Speech and dialogue systems","score":0.8984000086784363,"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/T12031","display_name":"Speech and dialogue systems","score":0.8984000086784363,"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/T10028","display_name":"Topic Modeling","score":0.042100001126527786,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.00860000029206276,"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/large-language-models","display_name":"large language models","score":0.7360000014305115},{"id":"https://openalex.org/keywords/semi-supervised-clustering","display_name":"semi-supervised clustering","score":0.8080000281333923},{"id":"https://openalex.org/keywords/dialogue-systems","display_name":"dialogue systems","score":0.6890000104904175},{"id":"https://openalex.org/keywords/user-utterances","display_name":"user utterances","score":0.5550000071525574}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7605999708175659},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7210000157356262},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6449999809265137},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.5242999792098999},{"id":"https://openalex.org/C154690210","wikidata":"https://www.wikidata.org/wiki/Q1668499","display_name":"Rewriting","level":2,"score":0.4684000015258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4528000056743622},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3490999937057495},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3481999933719635},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.32760000228881836},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2994000017642975},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.05913","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.05913","pdf_url":"https://arxiv.org/pdf/2511.05913","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.05913","doi":"https://doi.org/10.48550/arxiv.2511.05913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.05913","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,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.05913","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.05913","pdf_url":"https://arxiv.org/pdf/2511.05913","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G8618575630","display_name":null,"funder_award_id":"62302414","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416156928.pdf","grobid_xml":"https://content.openalex.org/works/W4416156928.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"New":[0],"intent":[1],"discovery":[2],"(NID)":[3],"seeks":[4],"to":[5,51,68,88,146,169],"recognize":[6],"both":[7,73,216],"new":[8],"and":[9,126,195,218],"known":[10],"intents":[11],"from":[12,72,178],"unlabeled":[13],"user":[14],"utterances,":[15],"which":[16,115],"finds":[17],"prevalent":[18],"use":[19],"in":[20,114,203],"practical":[21],"dialogue":[22],"systems.":[23],"Existing":[24],"works":[25],"towards":[26],"NID":[27,202],"mainly":[28],"adopt":[29],"a":[30,64,99],"cascaded":[31,65],"architecture,":[32],"wherein":[33],"the":[34,40,48,70,80,133,155,159,204],"first":[35,142],"stage":[36],"focuses":[37],"on":[38],"encoding":[39],"utterances":[41,131],"into":[42,55,144],"informative":[43],"text":[44,127],"embeddings":[45,54,128],"beforehand,":[46],"while":[47],"latter":[49],"is":[50],"group":[52],"similar":[53],"clusters":[56,179],"(i.e.,":[57],"intents),":[58],"typically":[59],"by":[60,121],"K-Means.":[61],"However,":[62],"such":[63],"pipeline":[66],"fails":[67],"leverage":[69],"feedback":[71],"steps":[74],"for":[75,105,151,182,199],"mutual":[76],"refinement,":[77],"and,":[78],"meanwhile,":[79],"embedding-only":[81],"clustering":[82,101,116],"overlooks":[83],"nuanced":[84],"textual":[85],"semantics,":[86],"leading":[87],"suboptimal":[89],"performance.":[90],"To":[91],"bridge":[92],"this":[93,95],"gap,":[94],"paper":[96],"proposes":[97],"NILC,":[98],"novel":[100],"framework":[102],"specially":[103],"catered":[104],"effective":[106],"NID.":[107],"Particularly,":[108],"NILC":[109,141,210,223],"follows":[110],"an":[111],"iterative":[112],"workflow,":[113],"assignments":[117],"are":[118,166],"judiciously":[119],"updated":[120],"carefully":[122],"refining":[123],"cluster":[124,184],"centroids":[125,150,161],"of":[129,135,158,162,233],"uncertain":[130],"with":[132],"aid":[134],"large":[136],"language":[137],"models":[138],"(LLMs).":[139],"Specifically,":[140],"taps":[143],"LLMs":[145,165],"create":[147],"additional":[148],"semantic":[149],"clusters,":[152],"thereby":[153],"enriching":[154],"contextual":[156],"semantics":[157],"Euclidean":[160],"embeddings.":[163],"Moreover,":[164],"then":[167],"harnessed":[168],"augment":[170],"hard":[171],"samples":[172],"(ambiguous":[173],"or":[174],"terse":[175],"utterances)":[176],"identified":[177],"via":[180],"rewriting":[181],"subsequent":[183],"correction.":[185],"Further,":[186],"we":[187],"inject":[188],"supervision":[189],"signals":[190],"through":[191],"non-trivial":[192],"techniques":[193],"seeding":[194],"soft":[196],"must":[197],"links":[198],"more":[200],"accurate":[201],"semi-supervised":[205,219],"setting.":[206],"Extensive":[207],"experiments":[208],"comparing":[209],"against":[211],"multiple":[212],"recent":[213],"baselines":[214],"under":[215],"unsupervised":[217],"settings":[220],"showcase":[221],"that":[222],"can":[224],"achieve":[225],"significant":[226],"performance":[227],"improvements":[228],"over":[229],"six":[230],"benchmark":[231],"datasets":[232],"diverse":[234],"domains":[235],"consistently.":[236]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2025-11-12T00:00:00"}