{"id":"https://openalex.org/W4417081414","doi":"https://doi.org/10.48550/arxiv.2509.14526","title":"Delta Knowledge Distillation for Large Language Models","display_name":"Delta Knowledge Distillation for Large Language Models","publication_year":2025,"publication_date":"2025-09-18","ids":{"openalex":"https://openalex.org/W4417081414","doi":"https://doi.org/10.48550/arxiv.2509.14526"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.14526","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.14526","pdf_url":"https://arxiv.org/pdf/2509.14526","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/2509.14526","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113340108","display_name":"Yihan Cao","orcid":"https://orcid.org/0009-0002-8408-790X","observed_orcids":["https://orcid.org/0009-0002-8408-790X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Yihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360946","display_name":"Yanbin Kang","orcid":"https://orcid.org/0000-0002-1503-0187","observed_orcids":["https://orcid.org/0000-0002-1503-0187","https://orcid.org/0009-0002-3608-0957"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Yanbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041900762","display_name":"Zhengming Xing","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Zhengming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088186926","display_name":"Ruijie Jiang","orcid":"https://orcid.org/0000-0002-4052-914X","observed_orcids":["https://orcid.org/0000-0002-4052-914X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Ruijie","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/T10028","display_name":"Topic Modeling","score":0.6542999744415283,"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":0.6542999744415283,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.1876000016927719,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0949999988079071,"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/knowledge-distillation","display_name":"knowledge distillation","score":0.9990000128746033},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9710000157356262},{"id":"https://openalex.org/keywords/distribution-shift","display_name":"distribution shift","score":0.9359999895095825},{"id":"https://openalex.org/keywords/supervised-fine-tuning","display_name":"supervised fine-tuning","score":0.7720000147819519},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.7630000114440918},{"id":"https://openalex.org/keywords/kullback-leibler-divergence","display_name":"Kullback-Leibler divergence","score":0.6620000004768372}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6442999839782715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6327000260353088},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5784000158309937},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5633999705314636},{"id":"https://openalex.org/C2778023277","wikidata":"https://www.wikidata.org/wiki/Q321703","display_name":"Premise","level":2,"score":0.5393999814987183},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5134000182151794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.4708999991416931},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.40560001134872437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3955000042915344},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27149999141693115},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.14526","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.14526","pdf_url":"https://arxiv.org/pdf/2509.14526","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.2509.14526","doi":"https://doi.org/10.48550/arxiv.2509.14526","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.14526","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:2509.14526","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.14526","pdf_url":"https://arxiv.org/pdf/2509.14526","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Knowledge":[0,87],"distillation":[1],"(KD)":[2],"is":[3],"a":[4,17,22,71,90],"widely":[5],"adopted":[6],"approach":[7],"for":[8],"compressing":[9],"large":[10,18,30],"neural":[11],"networks":[12],"by":[13,107],"transferring":[14],"knowledge":[15],"from":[16],"teacher":[19,46,62],"model":[20],"to":[21,101],"smaller":[23],"student":[24,42,58,100,132],"model.":[25],"In":[26],"the":[27,38,66,99,110,116,138],"context":[28],"of":[29,93,137],"language":[31],"models,":[32],"token":[33,94],"level":[34,95],"KD,":[35],"typically":[36],"minimizing":[37],"KL":[39],"divergence":[40],"between":[41],"output":[43,47,59,63],"distribution":[44,60,64],"and":[45,61],"distribution,":[48],"has":[49],"shown":[50],"strong":[51],"empirical":[52],"performance.":[53],"However,":[54],"prior":[55],"work":[56],"assumes":[57],"share":[65],"same":[67],"optimal":[68,104],"representation":[69,105],"space,":[70],"premise":[72],"that":[73,97,127],"may":[74],"not":[75],"hold":[76],"in":[77],"many":[78],"cases.":[79],"To":[80],"solve":[81],"this":[82],"problem,":[83],"we":[84],"propose":[85],"Delta":[86,113,128],"Distillation":[88],"(Delta-KD),":[89],"novel":[91],"extension":[92],"KD":[96,129],"encourages":[98],"approximate":[102],"an":[103],"space":[106],"explicitly":[108],"preserving":[109,135],"distributional":[111],"shift":[112],"introduced":[114],"during":[115],"teacher's":[117,139],"supervised":[118],"finetuning":[119],"(SFT).":[120],"Empirical":[121],"results":[122],"on":[123],"ROUGE":[124],"metrics":[125],"demonstrate":[126],"substantially":[130],"improves":[131],"performance":[133],"while":[134],"more":[136],"knowledge.":[140]},"counts_by_year":[],"updated_date":"2026-09-29T14:09:16.559479","created_date":"2025-10-10T00:00:00"}
