{"id":"https://openalex.org/W7166811045","doi":"https://doi.org/10.18653/v1/2026.acl-long.1526","title":"Neuron-Aware Active Few-Shot Learning for LLMs","display_name":"Neuron-Aware Active Few-Shot Learning for LLMs","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166811045","doi":"https://doi.org/10.18653/v1/2026.acl-long.1526"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.acl-long.1526","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1526","pdf_url":"https://aclanthology.org/2026.acl-long.1526.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.acl-long.1526.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139788953","display_name":"Zhuowei Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuowei Chen","raw_affiliation_strings":["University of Pittsburgh , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh , USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139799630","display_name":"Liwei Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liwei Chen","raw_affiliation_strings":["University of Pittsburgh , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh , USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139747661","display_name":"Christian Schunn","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christian Schunn","raw_affiliation_strings":["University of Pittsburgh , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh , USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139829878","display_name":"Raquel Coelho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raquel Coelho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139835804","display_name":"Xiang Lorraine Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang Lorraine Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":0.80114906,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"33047","last_page":"33062"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.618399977684021,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.618399977684021,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.07609999924898148,"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/T10320","display_name":"Neural Networks and Applications","score":0.029400000348687172,"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/annotation","display_name":"Annotation","score":0.5751000046730042},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5508999824523926},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4749000072479248},{"id":"https://openalex.org/keywords/diversity","display_name":"Diversity (politics)","score":0.42489999532699585},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.38179999589920044}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5931000113487244},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5751000046730042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5666000247001648},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5508999824523926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5202999711036682},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C2911011789","wikidata":"https://www.wikidata.org/wiki/Q130741","display_name":"Hallucinating","level":2,"score":0.2662999927997589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.acl-long.1526","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1526","pdf_url":"https://aclanthology.org/2026.acl-long.1526.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.acl-long.1526","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1526","pdf_url":"https://aclanthology.org/2026.acl-long.1526.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166811045.pdf","grobid_xml":"https://content.openalex.org/works/W7166811045.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Active":[0,77],"Few-Shot":[1,78],"Learning":[2,79],"(AFSL)":[3],"adapts":[4],"LLMs":[5,130],"to":[6,89,97,132],"specialized":[7],"domains":[8],"by":[9,134],"identifying":[10,124],"the":[11,40,83,175,178],"most":[12],"valuable":[13],"unlabeled":[14],"samples":[15,129],"for":[16,39,116],"annotation":[17,26],"and":[18,101,126,148,167],"use":[19],"as":[20,44],"few-shot":[21,110,128],"demonstrations,":[22],"effectively":[23],"reducing":[24],"human":[25],"costs":[27],"while":[28,120],"promoting":[29],"high":[30],"performance.However,":[31],"existing":[32,153],"methods":[33],"typically":[34],"rely":[35],"on":[36,54,123,138],"output-level":[37,87],"signals":[38],"sample":[41,99,111],"identification,":[42],"such":[43],"predictive":[45],"entropy":[46],"or":[47],"semantic":[48],"similarities":[49],"with":[50,113],"test-time":[51],"data":[52],"based":[53],"external":[55,172],"embeddings,":[56,173],"which":[57,63],"often":[58],"overlook":[59],"models'":[60,90],"internal":[61,91,160],"dynamics":[62],"could":[64],"pinpoint":[65],"specific":[66],"knowledge":[67],"gaps.To":[68],"bridge":[69],"this":[70],"gap,":[71],"we":[72],"propose":[73],"NEUFS,":[74],"a":[75,103,164],"Neuron-Aware":[76],"framework":[80],"that":[81,142,159],"shifts":[82],"selection":[84,105,169],"paradigm":[85],"from":[86],"proxies":[88],"dynamics.NEUFS":[92],"utilizes":[93],"neuron":[94,114,136,161],"activation":[95],"patterns":[96,115],"represent":[98],"directly,":[100],"includes":[102],"dual-criteria":[104],"strategy":[106],"that:":[107],"(1)":[108],"ensures":[109],"diversity":[112],"broader":[117],"example":[118],"coverage,":[119],"(2)":[121],"prioritizing":[122],"informative":[125],"challenging":[127],"tend":[131],"hallucinate":[133],"quantifying":[135],"consensus.Experiments":[137],"three":[139],"datasets":[140],"demonstrate":[141],"NEUFS":[143],"excels":[144],"in":[145],"both":[146],"reasoning":[147],"text":[149],"classification":[150],"tasks,":[151],"outperforming":[152],"AFSL":[154],"baselines.Ablation":[155],"studies":[156],"further":[157],"highlight":[158],"activations":[162],"provide":[163],"more":[165],"principled":[166],"effective":[168],"signal":[170],"than":[171],"validating":[174],"superiority":[176],"of":[177],"proposed":[179],"NEUFS.":[180]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}