{"id":"https://openalex.org/W4414360289","doi":"https://doi.org/10.24963/ijcai.2025/687","title":"Latte: Transfering LLMs' Latent-level Knowledge for Few-shot Tabular Learning","display_name":"Latte: Transfering LLMs' Latent-level Knowledge for Few-shot Tabular Learning","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360289","doi":"https://doi.org/10.24963/ijcai.2025/687"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/687","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013343127","display_name":"R. S. Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]},{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruxue Shi","raw_affiliation_strings":["Jilin University"],"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101084898","display_name":"Hengrui Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengrui Gu","raw_affiliation_strings":["Jilin University"],"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055076829","display_name":"Hangting Ye","orcid":"https://orcid.org/0000-0001-6920-4181"},"institutions":[{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]},{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hangting Ye","raw_affiliation_strings":["Jilin University"],"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026565649","display_name":"Yiwei Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiwei Dai","raw_affiliation_strings":["Jilin University"],"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000573596","display_name":"Xu Shen","orcid":"https://orcid.org/0000-0002-2356-7814"},"institutions":[{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]},{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Shen","raw_affiliation_strings":["Jilin University"],"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101803701","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0002-3485-3525"},"institutions":[{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]},{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Jilin University"],"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5013343127"],"corresponding_institution_ids":["https://openalex.org/I194450716","https://openalex.org/I4210136497"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13991632,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6173","last_page":"6181"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9991999864578247,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9991999864578247,"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/T13629","display_name":"Text Readability and Simplification","score":0.9865999817848206,"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":0.9728999733924866,"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/overfitting","display_name":"Overfitting","score":0.8296999931335449},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.5755000114440918},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5145999789237976},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5012999773025513},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3691999912261963},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3546999990940094},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3305000066757202},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.3255000114440918}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8296999931335449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7858999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6306999921798706},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.5755000114440918},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5357999801635742},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5145999789237976},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5012999773025513},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2718999981880188},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/687","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Few-shot":[0],"tabular":[1,42,100,154],"learning,":[2,101],"in":[3,35,169],"which":[4,55,62],"machine":[5],"learning":[6,155],"models":[7],"are":[8],"trained":[9],"with":[10,126],"a":[11,18,75,90,166],"limited":[12,119],"amount":[13],"of":[14,27,106,116,161],"labeled":[15,120,147],"data,":[16],"provides":[17],"cost-effective":[19],"approach":[20,168],"to":[21,64,88,118,137],"addressing":[22],"real-world":[23],"challenges.":[24],"The":[25],"advent":[26],"Large":[28],"Language":[29],"Models":[30],"(LLMs)":[31],"has":[32],"sparked":[33],"interest":[34],"leveraging":[36],"their":[37],"pre-trained":[38],"knowledge":[39,53,77,85],"for":[40],"few-shot":[41,153],"learning.":[43],"Despite":[44],"promising":[45],"results,":[46],"existing":[47,127],"approaches":[48],"either":[49],"rely":[50],"on":[51,151],"test-time":[52],"extraction,":[54],"introduces":[56],"undesirable":[57],"latency,":[58],"or":[59],"text-level":[60],"knowledge,":[61],"leads":[63],"unreliable":[65],"feature":[66,110],"engineering.":[67],"To":[68],"overcome":[69,138],"these":[70],"limitations,":[71],"we":[72],"propose":[73],"Latte,":[74,162],"training-time":[76],"extraction":[78],"framework":[79],"that":[80],"transfers":[81],"the":[82,103,114,139,158],"latent":[83],"prior":[84],"within":[86],"LLMs":[87],"optimize":[89],"more":[91],"generalized":[92],"downstream":[93,99],"model.":[94],"Latte":[95,123],"enables":[96],"general":[97],"knowledge-guided":[98],"facilitating":[102],"weighted":[104],"fusion":[105],"information":[107],"across":[108],"different":[109],"values":[111],"while":[112],"reducing":[113],"risk":[115],"overfitting":[117],"data.":[121],"Furthermore,":[122],"is":[124,174],"compatible":[125],"unsupervised":[128],"pre-training":[129],"paradigms":[130],"and":[131],"effectively":[132],"utilizes":[133],"available":[134,175],"unlabeled":[135],"samples":[136],"performance":[140,160],"limitations":[141],"imposed":[142],"by":[143],"an":[144],"extremely":[145],"small":[146],"dataset.":[148],"Extensive":[149],"experiments":[150],"various":[152],"benchmarks":[156],"demonstrate":[157],"superior":[159],"establishing":[163],"it":[164],"as":[165],"state-of-the-art":[167],"this":[170],"domain.":[171],"Our":[172],"code":[173],"at":[176],"https://github.com/ruxueshi/Latte.git.":[177]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
