{"id":"https://openalex.org/W7167639964","doi":"https://doi.org/10.48550/arxiv.2607.04071","title":"Beyond Multilingual Averages: MTEB-PT, a Benchmark for Portuguese Sentence Encoders","display_name":"Beyond Multilingual Averages: MTEB-PT, a Benchmark for Portuguese Sentence Encoders","publication_year":2026,"publication_date":"2026-07-05","ids":{"openalex":"https://openalex.org/W7167639964","doi":"https://doi.org/10.48550/arxiv.2607.04071"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.04071","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04071","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.04071","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140161276","display_name":"Lucas Hideki Takeuchi Okamura","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okamura, Lucas Hideki Takeuchi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058079727","display_name":"Alexandre Alcoforado","orcid":"https://orcid.org/0000-0003-3184-1534"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alcoforado, Alexandre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5069264027","display_name":"Anna Helena Reali Costa","orcid":"https://orcid.org/0000-0001-7309-4528"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Costa, Anna Helena Reali","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":0.7037000060081482,"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":0.7037000060081482,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.04729999974370003,"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":0.03180000185966492,"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/benchmark","display_name":"Benchmark (surveying)","score":0.8309999704360962},{"id":"https://openalex.org/keywords/portuguese","display_name":"Portuguese","score":0.6593000292778015},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6212000250816345},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5972999930381775},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5774000287055969},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5390999913215637},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4503999948501587},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42890000343322754}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8309999704360962},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.736299991607666},{"id":"https://openalex.org/C35219183","wikidata":"https://www.wikidata.org/wiki/Q5146","display_name":"Portuguese","level":2,"score":0.6593000292778015},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6431999802589417},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6212000250816345},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5972999930381775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5853999853134155},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5774000287055969},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5390999913215637},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4503999948501587},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42890000343322754},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3799000084400177},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3732999861240387},{"id":"https://openalex.org/C2780939345","wikidata":"https://www.wikidata.org/wiki/Q922399","display_name":"European Portuguese","level":3,"score":0.36730000376701355},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C2778880076","wikidata":"https://www.wikidata.org/wiki/Q750553","display_name":"Brazilian Portuguese","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.04071","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04071","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.04071","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04071","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"score":0.7554787993431091,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Portuguese":[0,37,44,85,159],"remains":[1,38],"underrepresented":[2],"in":[3,16,36,135],"text":[4],"embedding":[5,22,75],"evaluation,":[6],"despite":[7],"being":[8],"one":[9],"of":[10,50],"the":[11,17,143,178,197,200,204],"most":[12,146],"widely":[13],"spoken":[14],"languages":[15],"world.":[18],"As":[19],"a":[20,43,48,78],"result,":[21],"models":[23,76,108,157],"are":[24,113],"often":[25],"selected":[26],"based":[27],"on":[28,116,138,174],"English":[29],"or":[30],"multilingual":[31,90],"metrics,":[32],"while":[33,185],"their":[34,171],"effectiveness":[35],"unclear.":[39],"We":[40,65,195],"present":[41],"MTEB-PT,":[42],"benchmark":[45,68,125],"constructed":[46],"from":[47],"subset":[49],"MMTEB,":[51],"comprising":[52],"14":[53],"existing":[54],"datasets":[55],"across":[56,98],"Semantic":[57],"Textual":[58],"Similarity":[59],"(STS),":[60],"classification,":[61],"retrieval,":[62],"and":[63,73,107,122,162,189,203,206],"reranking.":[64,123],"use":[66],"this":[67,150],"to":[69],"evaluate":[70],"17":[71],"open-":[72],"closed-source":[74],"under":[77,192],"unified":[79],"protocol.":[80],"Our":[81],"results":[82],"show":[83],"that":[84,128,141],"performance":[86,97,134],"is":[87],"strongly":[88],"task-dependent:":[89],"rankings":[91],"do":[92],"not":[93],"reliably":[94],"predict":[95],"Portuguese-specific":[96],"task":[99,139],"families,":[100],"no":[101],"single":[102],"model":[103,133],"dominates":[104],"all":[105],"settings,":[106],"with":[109,158,177],"stronger":[110],"long-context":[111],"capacity":[112],"particularly":[114],"advantageous":[115],"longer-input":[117],"tasks":[118],"such":[119],"as":[120],"retrieval":[121,188],"The":[124],"also":[126,186],"shows":[127],"language-specific":[129],"fine-tuning":[130],"still":[131],"improves":[132],"Portuguese,":[136],"especially":[137],"types":[140],"match":[142],"adaptation":[144],"data":[145],"closely.":[147],"To":[148],"examine":[149],"effect,":[151],"we":[152],"fine-tune":[153],"three":[154],"representative":[155],"backbone":[156],"contrastive":[160],"supervision":[161,181],"Matryoshka":[163],"Representation":[164],"Learning":[165],"(MRL).":[166],"These":[167],"benchmark-informed":[168],"baselines":[169],"yield":[170],"strongest":[172],"gains":[173],"STS,":[175],"consistent":[176],"predominantly":[179],"symmetric":[180],"used":[182],"during":[183],"training,":[184],"improving":[187],"remaining":[190],"competitive":[191],"dimensional":[193],"truncation.":[194],"release":[196],"MTEB-PT":[198],"benchmark,":[199],"fine-tuned":[201],"models,":[202],"training":[205],"evaluation":[207],"code.":[208]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}
