{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "nllb-team-200-language-translation-2022", "title": "NLLB Team and colleagues: 200-language translation and FLORES-200 evaluation", "summary": "Combined low-resource language data mining and sparsely gated translation models with FLORES-200 evaluation across a large set of language directions.", "boundary": "Integrated translation-and-evaluation package, not an invention of mixture-of-experts. 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Costa-jussà", "James Cross", "Onur Çelebi", "Maha Elbayad", "Kenneth Heafield", "Kevin Heffernan", "Elahe Kalbassi", "Janice Lam", "Daniel Licht", "Jean Maillard", "Anna Sun", "Skyler Wang", "Guillaume Wenzek", "Al Youngblood", "Bapi Akula", "Loic Barrault", "Gabriel Mejia Gonzalez", "Prangthip Hansanti", "John Hoffman", "Semarley Jarrett", "Kaushik Ram Sadagopan", "Dirk Rowe", "Shannon Spruit", "Chau Tran", "Pierre Andrews", "Necip Fazil Ayan", "Shruti Bhosale", "Sergey Edunov", "Angela Fan", "Cynthia Gao", "Vedanuj Goswami", "Francisco Guzmán", "Philipp Koehn", "Alexandre Mourachko", "Christophe Ropers", "Safiyyah Saleem", "Holger Schwenk", "Jeff Wang" ], "authors_complete": true, "human_role_note": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. 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Translation quality and toxicity metrics are benchmark-specific, not equal quality across every language.", "domain": "Multilingual machine translation" }, "sources": [ { "citation": "No Language Left Behind: Scaling Human-Centered Machine Translation", "url": "https://arxiv.org/abs/2207.04672v3" }, { "citation": "NLLB Team and colleagues: 200-language translation and FLORES-200 evaluation — official research record", "url": "https://ai.meta.com/research/publications/no-language-left-behind-scaling-human-centered-machine-translation/" } ], "statement": "# NLLB Team and colleagues: 200-language translation and FLORES-200 evaluation\n\nCombined low-resource language data mining and sparsely gated translation models with FLORES-200 evaluation across a large set of language directions.\n\n## Scientific scope\n\nIntegrated translation-and-evaluation package, not an invention of mixture-of-experts. Translation quality and toxicity metrics are benchmark-specific, not equal quality across every language.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMarta R. Costa-jussà; James Cross; Onur Çelebi; Maha Elbayad; Kenneth Heafield; Kevin Heffernan; Elahe Kalbassi; Janice Lam; Daniel Licht; Jean Maillard; Anna Sun; Skyler Wang; Guillaume Wenzek; Al Youngblood; Bapi Akula; Loic Barrault; Gabriel Mejia Gonzalez; Prangthip Hansanti; John Hoffman; Semarley Jarrett; Kaushik Ram Sadagopan; Dirk Rowe; Shannon Spruit; Chau Tran; Pierre Andrews; Necip Fazil Ayan; Shruti Bhosale; Sergey Edunov; Angela Fan; Cynthia Gao; Vedanuj Goswami; Francisco Guzmán; Philipp Koehn; Alexandre Mourachko; Christophe Ropers; Safiyyah Saleem; Holger Schwenk; Jeff Wang\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672v3)\n- [NLLB Team and colleagues: 200-language translation and FLORES-200 evaluation — official research record](https://ai.meta.com/research/publications/no-language-left-behind-scaling-human-centered-machine-translation/)\n", "summary": "Combined low-resource language data mining and sparsely gated translation models with FLORES-200 evaluation across a large set of language directions.", "title": "NLLB Team and colleagues: 200-language translation and FLORES-200 evaluation" }, "record_id": "urn:uuid:3a43c6a1-7771-47f5-aa1b-4651559ac542", "version": 1, "source_ref": { "record_id": "urn:uuid:3a43c6a1-7771-47f5-aa1b-4651559ac542", "version": 1, "content_hash": "sha256:abf3e202f7bccb46caa4c8cf06f683ba0c5439e912a04dcd7908ef351feebe6a" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:ce49baeb52089f5e567fcbb05554012ce79d3a67c87979f2411297a0b47b159e" }, "target": { "record_id": "urn:uuid:3a43c6a1-7771-47f5-aa1b-4651559ac542", "version": 1, "content_hash": "sha256:abf3e202f7bccb46caa4c8cf06f683ba0c5439e912a04dcd7908ef351feebe6a" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "nllb-team-200-language-translation-2022", "entry_id": "nllb-team-200-language-translation-2022", "revision": 1, "title": "NLLB Team and colleagues: 200-language translation and FLORES-200 evaluation", "context": "Multilingual machine translation", "track": "Human-designed research method or resource", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Combined low-resource language data mining and sparsely gated translation models with FLORES-200 evaluation across a large set of language directions.", "scope": "Combined low-resource language data mining and sparsely gated translation models with FLORES-200 evaluation across a large set of language directions. Integrated translation-and-evaluation package, not an invention of mixture-of-experts. Translation quality and toxicity metrics are benchmark-specific, not equal quality across every language.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Integrated translation-and-evaluation package, not an invention of mixture-of-experts. Translation quality and toxicity metrics are benchmark-specific, not equal quality across every language.", "references": [ { "title": "No Language Left Behind: Scaling Human-Centered Machine Translation", "url": "https://arxiv.org/abs/2207.04672v3", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "NLLB Team and colleagues: 200-language translation and FLORES-200 evaluation — official research record", "url": "https://ai.meta.com/research/publications/no-language-left-behind-scaling-human-centered-machine-translation/", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:3a43c6a1-7771-47f5-aa1b-4651559ac542", "version": 1, "content_hash": "sha256:abf3e202f7bccb46caa4c8cf06f683ba0c5439e912a04dcd7908ef351feebe6a" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "130000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }