{"id":"https://openalex.org/W4396820848","doi":"https://doi.org/10.48550/arxiv.2404.18466","title":"HFT: Half Fine-Tuning for Large Language Models","display_name":"HFT: Half Fine-Tuning for Large Language Models","publication_year":2024,"publication_date":"2024-04-29","ids":{"openalex":"https://openalex.org/W4396820848","doi":"https://doi.org/10.48550/arxiv.2404.18466"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.18466","pdf_url":"https://arxiv.org/pdf/2404.18466","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2404.18466","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113204589","display_name":"Tingfeng Hui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hui, Tingfeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5097372866","display_name":"Zhenyu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhenyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101351951","display_name":"Shuohuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuohuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016651990","display_name":"Weiran Xu","orcid":"https://orcid.org/0000-0002-9416-7666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Weiran","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100966870","display_name":"Yu Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055027240","display_name":"Hua Wu","orcid":"https://orcid.org/0000-0001-6768-3940"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Hua","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":83},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9155,"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":0.9155,"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":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47255656}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.18466","pdf_url":"https://arxiv.org/pdf/2404.18466","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.18466","pdf_url":"https://arxiv.org/pdf/2404.18466","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4396701345","https://openalex.org/W4396696052","https://openalex.org/W4395014643","https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2382290278","https://openalex.org/W2376932109","https://openalex.org/W2358668433","https://openalex.org/W2001405890"],"abstract_inverted_index":{"Large":[0],"language":[1,24],"models":[2],"(LLMs)":[3],"with":[4,28,178,200],"one":[5],"or":[6,26,45],"more":[7],"fine-tuning":[8,93,153],"phases":[9],"have":[10],"become":[11],"a":[12,89,123,138,195],"necessary":[13],"step":[14],"to":[15,21,95,107,117],"unlock":[16],"various":[17],"capabilities,":[18],"enabling":[19],"LLMs":[20,70],"follow":[22],"natural":[23],"instructions":[25],"align":[27],"human":[29],"preferences.":[30],"However,":[31],"it":[32],"carries":[33],"the":[34,42,46,75,97,103,112,127,133,143,170,185,191],"risk":[35],"of":[36,74,102,129,175,197],"catastrophic":[37],"forgetting":[38,98,186],"during":[39],"sequential":[40],"training,":[41],"parametric":[43],"knowledge":[44],"ability":[47],"learned":[48],"in":[49,194,205],"previous":[50,119],"stages":[51],"may":[52],"be":[53,148],"overwhelmed":[54],"by":[55,65,79],"incoming":[56],"training":[57,206],"data.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62,81],"find":[63],"that":[64],"regularly":[66],"resetting":[67],"partial":[68],"parameters,":[69],"can":[71],"restore":[72],"some":[73],"original":[76],"knowledge.":[77,120],"Inspired":[78],"this,":[80],"introduce":[82],"Half":[83],"Fine-Tuning":[84],"(HFT)":[85],"for":[86,91],"LLMs,":[87],"as":[88,137],"substitute":[90],"full":[92],"(FFT),":[94],"mitigate":[96],"issues,":[99],"where":[100],"half":[101,114],"parameters":[104],"are":[105,115],"selected":[106],"learn":[108],"new":[109],"tasks":[110],"while":[111],"other":[113],"frozen":[116],"remain":[118],"We":[121],"provide":[122],"feasibility":[124],"analysis":[125,158],"from":[126],"perspective":[128],"optimization":[130],"and":[131,157,165,173],"interpret":[132],"parameter":[134],"selection":[135],"operation":[136],"regularization":[139],"term.":[140],"Without":[141],"changing":[142],"model":[144],"architecture,":[145],"HFT":[146,180],"could":[147],"seamlessly":[149],"integrated":[150],"into":[151],"existing":[152],"frameworks.":[154],"Extensive":[155],"experiments":[156],"on":[159],"supervised":[160],"fine-tuning,":[161],"direct":[162],"preference":[163],"optimization,":[164],"continual":[166],"learning":[167],"consistently":[168],"demonstrate":[169],"effectiveness,":[171],"robustness,":[172],"efficiency":[174],"HFT.":[176],"Compared":[177],"FFT,":[179],"not":[181],"only":[182],"significantly":[183],"alleviates":[184],"problem,":[187],"but":[188],"also":[189],"achieves":[190],"best":[192],"performance":[193],"series":[196],"downstream":[198],"benchmarks,":[199],"an":[201],"approximately":[202],"30%":[203],"reduction":[204],"time.":[207]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4396820848","counts_by_year":[],"updated_date":"2025-01-18T00:32:17.041150","created_date":"2024-05-11"}