{"id":"https://openalex.org/W7225628708","doi":"https://doi.org/10.18653/2026.findings-aacl.179","title":"Efficient Many-Shot In-Context Learning with Task-Level Compression and Instance-Level Retrieval","display_name":"Efficient Many-Shot In-Context Learning with Task-Level Compression and Instance-Level Retrieval","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7225628708","doi":"https://doi.org/10.18653/2026.findings-aacl.179"},"language":"en","primary_location":{"id":"doi:10.18653/2026.findings-aacl.179","doi":"https://doi.org/10.18653/2026.findings-aacl.179","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.findings-aacl.179","pdf_url":"https://aclanthology.org/2026.findings-aacl.179.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-aacl.179.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5095084156","display_name":"Masayuki Kawarada","orcid":"https://orcid.org/0009-0009-2467-9429","observed_orcids":["https://orcid.org/0009-0009-2467-9429"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masayuki Kawarada","raw_affiliation_strings":[", Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":", Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071438625","display_name":"Ukyo Honda","orcid":"https://orcid.org/0000-0002-4894-9886","observed_orcids":["https://orcid.org/0000-0002-4894-9886"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ukyo Honda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075222372","display_name":"Sho Takase","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sho Takase","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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3547","last_page":"3555"},"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":0.6015999913215637,"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":0.6015999913215637,"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/T10028","display_name":"Topic Modeling","score":0.35350000858306885,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.01510000042617321,"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-compression","display_name":"context compression","score":0.6850000023841858},{"id":"https://openalex.org/keywords/demonstration-retrieval","display_name":"demonstration retrieval","score":0.871999979019165},{"id":"https://openalex.org/keywords/prompt-efficiency","display_name":"prompt efficiency","score":0.6959999799728394},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9020000100135803},{"id":"https://openalex.org/keywords/inference-cost-reduction","display_name":"inference cost reduction","score":0.39899998903274536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6326000094413757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5041999816894531},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.4918999969959259},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.2597000002861023},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2554999887943268},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24799999594688416}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/2026.findings-aacl.179","doi":"https://doi.org/10.18653/2026.findings-aacl.179","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.findings-aacl.179","pdf_url":"https://aclanthology.org/2026.findings-aacl.179.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/2026.findings-aacl.179","doi":"https://doi.org/10.18653/2026.findings-aacl.179","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.findings-aacl.179","pdf_url":"https://aclanthology.org/2026.findings-aacl.179.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225628708.pdf","grobid_xml":"https://content.openalex.org/works/W7225628708.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W2794325560","https://openalex.org/W3210904070","https://openalex.org/W4414939355","https://openalex.org/W6947991961","https://openalex.org/W7084400772"],"related_works":["https://openalex.org/W4415949687","https://openalex.org/W6948242931","https://openalex.org/W7214316994","https://openalex.org/W4378718471","https://openalex.org/W2944870588","https://openalex.org/W7196991692","https://openalex.org/W7220793962","https://openalex.org/W2923561138","https://openalex.org/W7221049828","https://openalex.org/W7164327431"],"abstract_inverted_index":{"Many-shot":[0],"in-context":[1],"learning":[2],"(ICL)":[3],"lets":[4],"large":[5],"language":[6],"models":[7],"exploit":[8],"hundreds":[9],"or":[10],"thousands":[11],"of":[12,28,91,117],"demonstrations,":[13],"but":[14,141],"its":[15],"cost":[16],"grows":[17],"with":[18],"the":[19,80,107,113,118],"prompt.We":[20],"argue":[21],"that":[22,104,131],"many-shot":[23,120],"demonstrations":[24,72],"carry":[25],"two":[26,114],"kinds":[27],"information:":[29],"task-level":[30],"information,":[31,44],"such":[32,45],"as":[33,46,73],"task":[34],"structure,":[35],"which":[36,53],"emerges":[37],"only":[38],"across":[39],"many":[40],"examples,":[41],"and":[42,50,134],"instance-level":[43],"exact":[47],"inputoutput":[48],"associations":[49],"rare":[51],"exceptions,":[52],"resides":[54],"in":[55],"individual":[56],"demonstrations.Existing":[57],"efficiency":[58],"methods":[59],"commit":[60],"all":[61],"information":[62,92,108],"to":[63],"a":[64,70,84,123],"single":[65],"placement:":[66],"demonstration":[67],"retrieval":[68],"keeps":[69],"few":[71],"raw":[74],"text,":[75],"while":[76,111],"context":[77],"compression":[78],"places":[79],"full":[81,119],"pool":[82],"into":[83],"compact":[85],"memory.We":[86],"study":[87],"where":[88],"each":[89,97,139],"kind":[90],"should":[93],"be":[94],"placed.On":[95],"ManyI-CLBench,":[96],"one-sided":[98],"method":[99],"falls":[100],"short":[101],"on":[102,106],"tasks":[103,128],"depend":[105],"it":[109],"discards,":[110],"combining":[112],"retains":[115],"much":[116,124],"performance":[121],"at":[122],"smaller":[125],"inference":[126],"budget.Synthetic":[127],"further":[129],"show":[130],"compressed":[132],"memory":[133],"retrieved":[135],"examples":[136],"can":[137],"complement":[138],"other":[140],"sometimes":[142],"interfere.":[143]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-10T00:00:00"}
