{"id":"https://openalex.org/W7128644172","doi":"https://doi.org/10.48550/arxiv.2602.09987","title":"Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions","display_name":"Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions","publication_year":2026,"publication_date":"2026-02-10","ids":{"openalex":"https://openalex.org/W7128644172","doi":"https://doi.org/10.48550/arxiv.2602.09987"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.09987","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125659156","display_name":"J Rosser","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rosser, J","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051243028","display_name":"Robert Kirk","orcid":"https://orcid.org/0000-0002-6541-5915"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kirk, Robert","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023508792","display_name":"Edward Grefenstette","orcid":"https://orcid.org/0000-0003-1164-8809"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grefenstette, Edward","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125667206","display_name":"Jakob Foerster","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Foerster, Jakob","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060010430","display_name":"Laura Ruis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruis, Laura","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5125659156"],"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/T10028","display_name":"Topic Modeling","score":0.44780001044273376,"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/T10028","display_name":"Topic Modeling","score":0.44780001044273376,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.17720000445842743,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.09600000083446503,"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/interpretability","display_name":"Interpretability","score":0.9302999973297119},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7148000001907349},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6789000034332275},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5353000164031982},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5214999914169312},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4805000126361847},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.4350999891757965}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9302999973297119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7689999938011169},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7148000001907349},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6789000034332275},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5353000164031982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5303999781608582},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5214999914169312},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48339998722076416},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34389999508857727},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.09987","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.09987","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.09987","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.09987","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Influence":[0],"functions":[1],"are":[2],"commonly":[3],"used":[4],"to":[5,9,30,34,69,154],"attribute":[6],"model":[7,21,42,141,160],"behavior":[8,43,90],"training":[10,17,35,75,155,166],"documents.":[11],"We":[12,47,92,174],"explore":[13],"the":[14,74,81,124,140,163,176],"reverse:":[15],"crafting":[16],"data":[18,51,156,167],"that":[19,37,63,95,150],"induces":[20],"behavior.":[22],"Our":[23],"framework,":[24],"Infusion,":[25],"uses":[26],"scalable":[27],"influence-function":[28],"approximations":[29],"compute":[31],"small":[32,86],"perturbations":[33],"documents":[36,76],"induce":[38],"targeted":[39],"changes":[40],"in":[41],"through":[44],"parameter":[45],"shifts.":[46],"evaluate":[48],"Infusion":[49,68,96],"on":[50],"poisoning":[52],"tasks":[53],"across":[54,98],"vision":[55],"and":[56,129,171],"language":[57,116],"domains.":[58],"On":[59],"CIFAR-10,":[60],"we":[61,118],"show":[62,149],"making":[64],"subtle":[65,152],"edits":[66,153],"via":[67],"just":[70],"0.2%":[71],"(100/45,000)":[72],"of":[73,83,88,126,165],"can":[77,108,157],"be":[78],"competitive":[79],"with":[80],"baseline":[82],"inserting":[84],"a":[85,104],"number":[87],"explicit":[89],"examples.":[91],"also":[93],"find":[94],"transfers":[97],"architectures":[99],"(ResNet":[100],"$\\leftrightarrow$":[101],"CNN),":[102],"suggesting":[103],"single":[105],"poisoned":[106],"corpus":[107],"affect":[109],"multiple":[110],"independently":[111],"trained":[112],"models.":[113],"In":[114],"preliminary":[115],"experiments,":[117],"characterize":[119],"when":[120,130],"our":[121],"approach":[122],"increases":[123],"probability":[125],"target":[127],"behaviors":[128,139],"it":[131,134],"fails,":[132],"finding":[133],"most":[135],"effective":[136],"at":[137],"amplifying":[138],"has":[142],"already":[143],"learned.":[144],"Taken":[145],"together,":[146],"these":[147],"results":[148],"small,":[151],"systematically":[158],"shape":[159],"behavior,":[161],"underscoring":[162],"importance":[164],"interpretability":[168],"for":[169],"adversaries":[170],"defenders":[172],"alike.":[173],"provide":[175],"code":[177],"here:":[178],"https://github.com/jrosseruk/infusion.":[179]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-12T00:00:00"}
