{"id":"https://openalex.org/W7166817644","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1527","title":"Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning","display_name":"Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166817644","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1527"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.1527","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1527","pdf_url":"https://aclanthology.org/2026.findings-acl.1527.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: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.1527.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139727667","display_name":"Jianxin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianxin Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139734600","display_name":"Yilu Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yilu Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139830990","display_name":"Teng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teng Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139738451","display_name":"Pei Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pei Guo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139842492","display_name":"Juntao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Juntao Li","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.80716034,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"30543","last_page":"30566"},"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":0.21080000698566437,"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":0.21080000698566437,"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/T10656","display_name":"Child and Animal Learning Development","score":0.07930000126361847,"subfield":{"id":"https://openalex.org/subfields/3204","display_name":"Developmental and Educational Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.04270000010728836,"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/context","display_name":"Context (archaeology)","score":0.5651000142097473},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.2842000126838684},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2655999958515167},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.2556000053882599},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.24879999458789825}],"concepts":[{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5651000142097473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5432000160217285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4311999976634979},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.24879999458789825},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.24140000343322754},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.23549999296665192},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.23350000381469727}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.1527","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1527","pdf_url":"https://aclanthology.org/2026.findings-acl.1527.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: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.1527","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1527","pdf_url":"https://aclanthology.org/2026.findings-acl.1527.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: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.44493988156318665}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324720","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166817644.pdf","grobid_xml":"https://content.openalex.org/works/W7166817644.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Implicit":[0],"In-Context":[1],"Learning":[2],"compresses":[3],"demonstration":[4],"examples":[5],"into":[6,14,35,97],"a":[7,36,63,103,114],"single":[8,104],"context":[9,39,81,92,147],"vector":[10,148],"and":[11,150],"injects":[12],"it":[13],"the":[15,98,119],"model's":[16,99],"activation":[17],"space,":[18],"achieving":[19],"few-shot-level":[20],"performance":[21],"at":[22],"near":[23],"zero-shot":[24],"inference":[25],"cost.However,":[26],"existing":[27],"approaches":[28],"typically":[29],"aggregate":[30],"demonstrations":[31],"from":[32],"all":[33],"classes":[34],"shared,":[37],"tasklevel":[38],"vector,":[40],"capturing":[41],"global":[42],"task":[43],"information":[44,77],"but":[45],"without":[46,86],"explicitly":[47,73],"preserving":[48],"finegrained,":[49],"class-conditional":[50,91],"semantic":[51],"distinctions.In":[52],"this":[53],"work,":[54],"we":[55],"propose":[56],"Class-Conditional":[57],"Context":[58],"Vectors":[59],"(C":[60],"3":[61,124,142],"V),":[62],"simple":[64],"yet":[65],"effective":[66],"extension":[67],"to":[68,110],"implicit":[69],"in-context":[70],"learning":[71,158],"that":[72,140],"models":[74],"class-specific":[75],"contextual":[76,108],"by":[78],"constructing":[79],"separate":[80],"vectors":[82,93],"for":[83],"each":[84],"class,":[85],"relying":[87],"on":[88,126,159],"explicit":[89],"prompts.These":[90],"are":[94],"additively":[95],"injected":[96],"residual":[100],"streams":[101],"in":[102,113],"forward":[105],"pass,":[106],"enabling":[107],"contributions":[109],"be":[111],"modulated":[112],"class-aware":[115],"manner":[116],"while":[117],"keeping":[118],"backbone":[120],"frozen.We":[121],"evaluate":[122],"C":[123,141],"V":[125,143],"multiple":[127],"text":[128],"classification":[129],"benchmarks":[130],"across":[131],"several":[132],"families":[133],"of":[134],"large":[135],"language":[136],"models.Experimental":[137],"results":[138],"demonstrate":[139],"consistently":[144],"outperforms":[145],"task-level":[146],"baselines,":[149],"achieves":[151],"higher":[152],"average":[153],"accuracy":[154],"than":[155],"conventional":[156],"few-shot":[157],"most":[160],"models.":[161]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}