{"id":"https://openalex.org/W7154948845","doi":"https://doi.org/10.48550/arxiv.2604.16037","title":"Stochasticity in Tokenisation Improves Robustness","display_name":"Stochasticity in Tokenisation Improves Robustness","publication_year":2026,"publication_date":"2026-04-17","ids":{"openalex":"https://openalex.org/W7154948845","doi":"https://doi.org/10.48550/arxiv.2604.16037"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.16037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16037","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.2604.16037","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115616592","display_name":"Sophie Steger","orcid":"https://orcid.org/0000-0001-8766-3277"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Steger, Sophie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134069573","display_name":"Rui Li","orcid":"https://orcid.org/0000-0003-1668-9554"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088419102","display_name":"Sofiane Ennadir","orcid":"https://orcid.org/0000-0001-9969-4660"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ennadir, Sofiane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134084938","display_name":"Anya Sims","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sims, Anya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014200248","display_name":"Arno Solin","orcid":"https://orcid.org/0000-0002-0958-7886"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Solin, Arno","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015798259","display_name":"Franz Pernkopf","orcid":"https://orcid.org/0000-0002-6356-3367"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pernkopf, Franz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134026773","display_name":"Martin Trapp","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trapp, Martin","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.45170000195503235,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.45170000195503235,"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":0.14910000562667847,"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":0.13089999556541443,"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/robustness","display_name":"Robustness (evolution)","score":0.7861999869346619},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7258999943733215},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5685999989509583},{"id":"https://openalex.org/keywords/stochastic-process","display_name":"Stochastic process","score":0.44359999895095825},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.374099999666214},{"id":"https://openalex.org/keywords/stochastic-modelling","display_name":"Stochastic modelling","score":0.36419999599456787}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7861999869346619},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7258999943733215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.590399980545044},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5685999989509583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4562999904155731},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38370001316070557},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33340001106262207},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.31940001249313354},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2971999943256378},{"id":"https://openalex.org/C2986577269","wikidata":"https://www.wikidata.org/wiki/Q11306265","display_name":"Random noise","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2761000096797943}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.16037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16037","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.2604.16037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16037","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":{"The":[0],"widespread":[1],"adoption":[2],"of":[3,17,19,76,117],"large":[4],"language":[5],"models":[6,24],"(LLMs)":[7],"has":[8],"increased":[9],"concerns":[10],"about":[11],"their":[12],"robustness.":[13],"Vulnerabilities":[14],"in":[15],"perturbations":[16],"tokenisation":[18,30,42,131],"the":[20,115],"input":[21],"indicate":[22],"that":[23,40,47,92,127],"trained":[25,120],"with":[26,96,129],"a":[27,74,118],"deterministic":[28],"canonical":[29],"can":[31,43],"be":[32],"brittle":[33],"to":[34,51,63,103],"adversarial":[35,64,106],"attacks.":[36],"Recent":[37],"studies":[38],"suggest":[39],"stochastic":[41,59,99,130],"deliver":[44],"internal":[45],"representations":[46],"are":[48],"less":[49],"sensitive":[50],"perturbations.":[52,68,107],"In":[53],"this":[54,72],"paper,":[55],"we":[56],"analyse":[57],"how":[58],"tokenisations":[60,100,113],"affect":[61],"robustness":[62,102],"attacks":[65],"and":[66,82,87,94,105],"random":[67,104],"We":[69,90,125],"systematically":[70],"study":[71],"over":[73],"range":[75],"learning":[77],"regimes":[78],"(pre-training,":[79],"supervised":[80],"fine-tuning,":[81],"in-context":[83],"learning),":[84],"data":[85],"sets,":[86],"model":[88,122],"architectures.":[89],"show":[91],"pre-training":[93],"fine-tuning":[95],"uniformly":[97,110],"sampled":[98,111],"improve":[101],"Evaluating":[108],"on":[109],"non-canonical":[112],"reduces":[114],"accuracy":[116,133],"canonically":[119],"Llama-1b":[121],"by":[123],"29.8%.":[124],"find":[126],"training":[128],"preserves":[132],"without":[134],"increasing":[135],"inference":[136],"cost.":[137]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-21T00:00:00"}