{"id":"https://openalex.org/W4416034348","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1026","title":"ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs","display_name":"ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034348","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1026"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1026","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1026","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1026.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.1026.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114909883","display_name":"Zige Wang","orcid":"https://orcid.org/0000-0003-2895-7982"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zige Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037290686","display_name":"Qi Zhu","orcid":"https://orcid.org/0000-0001-7740-292X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi Zhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012014905","display_name":"Fei Mi","orcid":"https://orcid.org/0000-0001-6358-9922"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fei Mi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068736044","display_name":"M. Xu","orcid":"https://orcid.org/0000-0002-1891-8552"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minghui Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ruochun Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruochun Jin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102809598","display_name":"Wenjing Yang","orcid":"https://orcid.org/0000-0001-5026-9938"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenjing Yang","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":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"18867","last_page":"18880"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.20890000462532043,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.20890000462532043,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.06589999794960022,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.06319999694824219,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4961000084877014},{"id":"https://openalex.org/keywords/genetic-data","display_name":"Genetic data","score":0.27160000801086426},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.24650000035762787},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.21660000085830688}],"concepts":[{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4961000084877014},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4781999886035919},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3407000005245209},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.28519999980926514},{"id":"https://openalex.org/C2993807623","wikidata":"https://www.wikidata.org/wiki/Q7020","display_name":"Genetic data","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2653999924659729},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.24650000035762787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23420000076293945},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.227400004863739},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.21660000085830688}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.1026","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1026","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1026.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1026","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1026","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1026.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G160098265","display_name":null,"funder_award_id":"202401","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2572137687","display_name":null,"funder_award_id":"62302503","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G392688194","display_name":null,"funder_award_id":"62372459","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320311778","display_name":"China Association for Science and Technology","ror":"https://ror.org/035vmht26"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324150","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034348.pdf","grobid_xml":"https://content.openalex.org/works/W4416034348.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gradient-based":[0],"data":[1,10,44,61,78,84,115,157],"influence":[2,116],"approximation":[3],"has":[4],"been":[5],"leveraged":[6,104],"to":[7,32,105,120,134,153],"select":[8],"useful":[9],"samples":[11,62],"in":[12,35],"the":[13,20,25,58,76,82,110,123,154],"supervised":[14],"fine-tuning":[15,26],"of":[16,22,125],"large":[17],"language":[18],"models.However,":[19],"computation":[21],"gradients":[23],"throughout":[24],"process":[27],"requires":[28],"too":[29],"many":[30],"resources":[31],"be":[33,168],"feasible":[34],"practice.In":[36],"this":[37,107],"paper,":[38],"we":[39,71,80],"propose":[40],"an":[41],"efficient":[42],"gradientbased":[43],"selection":[45,85,158],"framework":[46],"with":[47,63],"clustering":[48,74],"and":[49,93,128,137],"a":[50,87,96,129],"modified":[51,100],"Upper":[52],"Confidence":[53],"Bound":[54],"(UCB)":[55],"algorithm.Based":[56],"on":[57,75,140],"intuition":[59],"that":[60,144],"similar":[64,69],"gradient":[65],"features":[66],"will":[67],"have":[68],"influences,":[70],"first":[72],"perform":[73],"training":[77],"pool.Then,":[79],"frame":[81],"inter-cluster":[83],"as":[86],"constrained":[88],"computing":[89,163],"budget":[90],"allocation":[91],"problem":[92],"consider":[94],"it":[95],"multi-armed":[97],"bandit":[98],"problem.A":[99],"UCB":[101],"algorithm":[102],"is":[103,118,132],"solve":[106],"problem.Specifically,":[108],"during":[109],"iterative":[111],"sampling":[112],"process,":[113],"historical":[114],"information":[117],"recorded":[119],"directly":[121],"estimate":[122],"distributions":[124],"each":[126],"cluster,":[127],"cold":[130],"start":[131],"adopted":[133],"balance":[135],"exploration":[136],"exploitation.Experimental":[138],"results":[139,152],"various":[141],"benchmarks":[142],"show":[143],"our":[145],"proposed":[146],"framework,":[147],"Clus-terUCB,":[148],"can":[149,167],"achieve":[150],"comparable":[151],"original":[155],"gradient-based":[156],"methods":[159],"while":[160],"greatly":[161],"reducing":[162],"consumption.The":[164],"code":[165],"implementation":[166],"found":[169],"at":[170],"https://github.com/ZigeW/ClusterUCB.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}