{"id":"https://openalex.org/W7161716070","doi":"https://doi.org/10.48550/arxiv.2605.16591","title":"How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning","display_name":"How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161716070","doi":"https://doi.org/10.48550/arxiv.2605.16591"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16591","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16591","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.16591","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136497835","display_name":"Entang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Entang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136486827","display_name":"Yiwei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093709203","display_name":"Aleksandra Bakalova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bakalova, Aleksandra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136503381","display_name":"Michael Hahn","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hahn, Michael","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":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.849399983882904,"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":0.849399983882904,"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":0.03280000016093254,"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/T10918","display_name":"Memory Processes and Influences","score":0.012900000438094139,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.597599983215332},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.593999981880188},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5799999833106995},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4465999901294708},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4242999851703644},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.3984000086784363}],"concepts":[{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.597599983215332},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.593999981880188},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5799999833106995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5738999843597412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.546999990940094},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3449000120162964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33250001072883606},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31929999589920044},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.29660001397132874},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.265500009059906}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16591","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16591","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.16591","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16591","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In-context":[0],"learning":[1],"(ICL)":[2],"excels":[3],"at":[4],"new":[5],"tasks":[6,39],"from":[7,59,105,118],"minimal":[8],"examples,":[9],"yet":[10],"we":[11,64],"still":[12],"lack":[13],"a":[14,22,48,99,140],"mechanistic":[15],"explanation":[16],"of":[17,51,144],"how":[18,145],"few-shot":[19,146],"prompts":[20,147],"shape":[21],"model's":[23],"function":[24],"vector":[25],"(FV)--a":[26],"causal":[27,100],"activation":[28],"direction":[29],"that":[30,66,88,109],"drives":[31],"task":[32],"behavior":[33],"on":[34,73],"the":[35,82,96],"ICL":[36],"query.":[37],"Across":[38],"and":[40,56,92],"models,":[41],"an":[42],"$n$-shot":[43],"FV":[44,115],"is":[45],"well-approximated":[46],"by":[47],"linear":[49],"combination":[50],"example-level":[52],"sub-FVs,":[53],"suggesting":[54],"additive":[55,133],"composable":[57],"contributions":[58,113],"individual":[60,69],"demonstrations.":[61],"Beyond":[62],"additivity,":[63],"show":[65],"models":[67],"contextualize":[68],"examples'":[70],"representations":[71],"based":[72],"prior":[74],"examples":[75,87],"to":[76,114],"adaptively":[77],"reweight":[78],"which":[79],"demonstrations":[80],"dominate":[81],"FV:":[83],"attention":[84,137],"shifts":[85],"toward":[86],"are":[89,126],"more":[90,127],"informative":[91],"less":[93],"ambiguous":[94,122],"under":[95],"context.":[97],"Finally,":[98],"decomposition":[101],"separates":[102],"Query-Key":[103,119],"routing":[104],"Value":[106],"updates,":[107],"finding":[108],"contextualization's":[110],"most":[111],"consistent":[112],"quality":[116],"arise":[117],"alignment--particularly":[120],"in":[121],"settings--while":[123],"Value-mediated":[124],"effects":[125],"heterogeneous.":[128],"Together,":[129],"these":[130],"results":[131],"unify":[132],"superposition":[134],"with":[135],"context-dependent":[136],"reweighting":[138],"into":[139],"mechanistic,":[141],"testable":[142],"account":[143],"implement":[148],"tasks.":[149]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
