{"id":"https://openalex.org/W7131633835","doi":"https://doi.org/10.48550/arxiv.2602.21652","title":"Sparsity Induction for Accurate Post-Training Pruning of Large Language Models","display_name":"Sparsity Induction for Accurate Post-Training Pruning of Large Language Models","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131633835","doi":"https://doi.org/10.48550/arxiv.2602.21652"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.21652","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041629141","display_name":"Minhao Jiang","orcid":"https://orcid.org/0000-0002-6812-0952"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Minhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126916944","display_name":"Zhikai Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhikai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126873641","display_name":"Xuewen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xuewen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126944243","display_name":"Jing Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Mengjuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Mengjuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5097357443","display_name":"Qingyi Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Qingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":0.6468999981880188,"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":0.6468999981880188,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.05730000138282776,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.02889999933540821,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.72079998254776},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.680400013923645},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5494999885559082},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.47209998965263367},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.45100000500679016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35749998688697815}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.72079998254776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7163000106811523},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.680400013923645},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5494999885559082},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.47209998965263367},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47110000252723694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4706999957561493},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.45100000500679016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3343999981880188},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.26269999146461487}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.21652","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.21652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.21652","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.21652","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6217886805534363,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,69],"have":[3],"demonstrated":[4],"capabilities":[5],"in":[6,16,56],"text":[7],"generation,":[8],"while":[9],"their":[10],"increasing":[11],"parameter":[12],"scales":[13],"present":[14],"challenges":[15],"computational":[17],"and":[18,76,104,134],"memory":[19],"efficiency.":[20],"Post-training":[21],"sparsity":[22,72,94,124],"(PTS),":[23],"which":[24,67,100],"reduces":[25],"model":[26,53,132],"cost":[27],"by":[28],"removing":[29],"weights":[30,51],"from":[31,125],"dense":[32,40],"networks,":[33],"is":[34],"an":[35],"effective":[36],"approach.":[37],"However,":[38],"native":[39],"matrices":[41],"lack":[42],"high":[43],"sparsity,":[44],"making":[45],"existing":[46,148],"approaches":[47],"that":[48,137],"directly":[49],"remove":[50],"disrupt":[52],"states,":[54],"resulting":[55],"unsatisfactory":[57],"performance":[58,146],"recovery":[59],"even":[60],"with":[61],"post-tuning.":[62],"We":[63],"propose":[64],"Sparsity":[65],"Induction,":[66],"promotes":[68],"toward":[70],"higher":[71],"at":[73],"both":[74],"distribution":[75,89],"feature":[77,114,123],"levels":[78],"before":[79],"pruning,":[80],"to":[81,121],"push":[82],"the":[83,88,113],"limits":[84],"of":[85],"PTS.":[86],"At":[87,112],"level,":[90,115],"we":[91,116],"enhance":[92],"distributional":[93],"through":[95],"mathematically":[96],"equivalent":[97],"scaling":[98],"transformations,":[99],"are":[101],"fully":[102],"absorbable":[103],"incur":[105],"no":[106],"extra":[107],"parameters":[108],"or":[109],"inference-time":[110],"overhead.":[111],"introduce":[117],"Spectral":[118],"Norm":[119],"Loss":[120],"promote":[122],"a":[126],"low-rank":[127],"perspective.":[128],"Experiments":[129],"across":[130],"diverse":[131],"architectures":[133],"tasks":[135],"demonstrate":[136],"our":[138],"method":[139],"further":[140],"enhances":[141],"sparsity-friendliness,":[142],"achieving":[143],"superior":[144],"pruning":[145],"over":[147],"approaches.":[149]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-27T00:00:00"}
