{"id":"https://openalex.org/W7163043553","doi":"https://doi.org/10.48550/arxiv.2605.31142","title":"On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets","display_name":"On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163043553","doi":"https://doi.org/10.48550/arxiv.2605.31142"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31142","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.2605.31142","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034185097","display_name":"Ana Gjorgjevikj","orcid":"https://orcid.org/0000-0002-5135-7718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gjorgjevikj, Ana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076065873","display_name":"Barbara Korou\u0161i\u0107 Seljak","orcid":"https://orcid.org/0000-0001-7597-2590"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seljak, Barbara Korou\u0161i\u0107","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082115266","display_name":"Tome Eftimov","orcid":"https://orcid.org/0000-0001-7330-1902"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eftimov, Tome","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/T10028","display_name":"Topic Modeling","score":0.32580000162124634,"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":0.32580000162124634,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.11659999936819077,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.10909999907016754,"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/benchmarking","display_name":"Benchmarking","score":0.8788999915122986},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8765000104904175},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6238999962806702},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.46970000863075256},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.40709999203681946},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3375000059604645}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8788999915122986},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8765000104904175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7005000114440918},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6238999962806702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.498199999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4934000074863434},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4171000123023987},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.2953999936580658},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C137726913","wikidata":"https://www.wikidata.org/wiki/Q7353550","display_name":"Robustness testing","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31142","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.2605.31142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31142","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":{"Large-scale":[0],"multilingual":[1,61],"text":[2],"embedding":[3],"models":[4,149,172],"play":[5],"crucial":[6],"role":[7],"in":[8,16,65,159],"both":[9],"research":[10],"and":[11,48,76,90,125,134,180],"industry,":[12],"yet":[13],"their":[14],"behavior":[15],"language-specific,":[17],"multi-task":[18],"settings":[19],"remains":[20,173],"insufficiently":[21],"understood.":[22],"Although":[23],"benchmarking":[24,105],"platforms":[25],"such":[26],"as":[27],"MTEB":[28],"report":[29],"results":[30,136,164],"across":[31,127,176],"more":[32],"than":[33],"250":[34],"languages,":[35],"conclusions":[36,106],"about":[37],"model":[38,62],"superiority":[39],"often":[40,151],"depend":[41],"on":[42,118],"implicit":[43],"choices":[44],"of":[45,60,71,84,103,171],"dataset":[46,88],"compositions":[47],"performance":[49,63],"aggregation":[50,95],"methods.":[51],"To":[52],"address":[53],"this":[54],"gap,":[55],"we":[56],"present":[57],"a":[58,68,168],"meta-study":[59],"robustness":[64,79,82,92],"MTEB,":[66],"applying":[67],"diverse":[69],"set":[70],"multi-criteria":[72],"decision-making":[73],"ranking":[74,178],"schemes":[75],"introducing":[77],"two":[78],"indicators:":[80],"dataset-composition":[81],"(sensitivity":[83,93],"rankings":[85],"to":[86,94],"changing":[87],"compositions)":[89],"ranking-scheme":[91],"method":[96],"change).":[97],"They":[98],"enable":[99],"systematic":[100],"sensitivity":[101],"analysis":[102,117],"whether":[104],"remain":[107],"stable":[108],"under":[109],"different":[110],"evaluation":[111],"designs.":[112],"We":[113],"conduct":[114],"an":[115],"in-depth":[116],"five":[119],"languages":[120],"(English,":[121],"French,":[122],"German,":[123],"Hindi,":[124],"Spanish)":[126],"nine":[128],"tasks":[129],"(e.g.,":[130,158],"classification,":[131],"clustering,":[132],"retrieval)":[133],"release":[135],"for":[137],"approximately":[138],"230":[139],"additional":[140],"languages.":[141],"The":[142],"task-specific":[143],"analyses":[144],"show":[145],"that":[146,166],"large-scale":[147],"LLM-based":[148],"are":[150],"robust":[152],"top":[153],"performers,":[154],"though":[155],"not":[156],"uniformly":[157],"retrieval":[160],"task),":[161],"while":[162],"task-agnostic":[163],"reveal":[165],"only":[167],"small":[169],"subset":[170],"consistently":[174],"strong":[175],"tasks,":[177],"schemes,":[179],"data":[181],"subsamples.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
