{"id":"https://openalex.org/W7140130333","doi":"https://doi.org/10.18653/v1/2026.eacl-long.140","title":"PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark","display_name":"PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140130333","doi":"https://doi.org/10.18653/v1/2026.eacl-long.140"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.eacl-long.140","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.140","pdf_url":"https://aclanthology.org/2026.eacl-long.140.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":"Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.eacl-long.140.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Robert Belanec","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Robert Belanec","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Branislav Pecher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Branislav Pecher","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ivan Srba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ivan Srba","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Maria Bielikova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maria Bielikova","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75080386,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3035","last_page":"3054"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11034","display_name":"Digital Filter Design and Implementation","score":0.06780000030994415,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11034","display_name":"Digital Filter Design and Implementation","score":0.06780000030994415,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.03370000049471855,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.01850000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.36070001125335693},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.26249998807907104},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.25929999351501465},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.2581999897956848},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.24799999594688416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.60589998960495},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3646000027656555},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29760000109672546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.24799999594688416},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.24379999935626984},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.23690000176429749}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.eacl-long.140","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.140","pdf_url":"https://aclanthology.org/2026.eacl-long.140.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":"Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.eacl-long.140","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.140","pdf_url":"https://aclanthology.org/2026.eacl-long.140.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":"Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2592723113","display_name":null,"funder_award_id":"311070AKF2","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G260610314","display_name":"Low Resource Artificial Intelligence","funder_award_id":"101136646","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G7758855706","display_name":null,"funder_award_id":"311070AKF2","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G941802975","display_name":null,"funder_award_id":"90254","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7140130333.pdf","grobid_xml":"https://content.openalex.org/works/W7140130333.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Despite":[0],"the":[1,35,45,105],"state-of-the-art":[2],"performance":[3],"of":[4,37,56],"Large":[5],"Language":[6],"Models":[7],"(LLMs)":[8],"achieved":[9],"on":[10,80],"many":[11],"tasks,":[12],"their":[13,25],"massive":[14],"scale":[15],"often":[16],"leads":[17],"to":[18,63],"high":[19],"computational":[20],"and":[21,59,61,90,99,118],"environmental":[22],"costs,":[23],"limiting":[24],"accessibility.Parameter-Efficient":[26],"Fine-Tuning":[27],"(PEFT)":[28],"methods":[29,79],"address":[30],"this":[31,66],"challenge":[32],"by":[33],"reducing":[34],"number":[36],"trainable":[38,114],"parameters":[39],"while":[40],"maintaining":[41],"strong":[42],"downstream":[43],"performance.Despite":[44],"advances":[46],"in":[47],"PEFT":[48,78,92,97,106],"methods,":[49],"current":[50],"evaluations":[51],"remain":[52],"limited":[53],"(in":[54],"terms":[55],"evaluated":[57],"models":[58],"datasets)":[60],"difficult":[62],"reproduce.To":[64],"bridge":[65],"gap,":[67],"we":[68,102],"introduce":[69,104],"PEFT-Bench,":[70],"a":[71],"unified":[72],"end-to-end":[73],"benchmark":[74],"for":[75,95],"evaluating":[76],"diverse":[77],"autoregressive":[81],"LLMs.We":[82],"demonstrate":[83],"its":[84],"usage":[85,121],"across":[86],"27":[87],"NLP":[88],"datasets":[89],"7":[91],"methods.To":[93],"account":[94],"different":[96],"training":[98,119],"inference":[100,116],"factors,":[101],"also":[103],"Soft":[107],"Cost":[108],"Penalties":[109],"(PSCP)":[110],"metric,":[111],"which":[112],"takes":[113],"parameters,":[115],"speed,":[117],"memory":[120],"into":[122],"account.":[123]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2026-02-27T00:00:00"}
