---
title: "VN-MTEB: Vietnamese Massive Text Embedding Benchmark"
description: "MMTEB offers just 18 Vietnamese datasets compared with over 300 for English, and existing… 28 references, cited by 4 papers here."
authors: ["Loc Pham", "Tung Luu", "Thu Vo", "Minh Nguyen", "Viet Hoang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.86/"
doi: "10.18653/v1/2026.findings-eacl.86"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pham-2026-vn-mteb-vietnamese-massive-text/"
---
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# VN-MTEB: Vietnamese Massive Text Embedding Benchmark

**Authors:** Loc Pham, Tung Luu, Thu Vo, [Minh Nguyen](https://md.research.elamur.ai/free/en/authors/minh-nguyen/) and Viet Hoang · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.86/) · [PDF](https://aclanthology.org/2026.findings-eacl.86.pdf) · [DOI 10.18653/v1/2026.findings-eacl.86](https://doi.org/10.18653/v1/2026.findings-eacl.86) · [OpenAlex](https://openalex.org/W7140132489)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/pham-2026-vn-mteb-vietnamese-massive-text/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Vietnamese, with over 100 million speakers, has few large evaluation resources for text embeddings. MMTEB offers just 18 Vietnamese datasets compared with over 300 for English, and existing Vietnamese corpora usually cover a single task or domain. The authors build VN-MTEB, which adds 41 Vietnamese datasets across six task types by machine-translating English MTEB data. Their pipeline has three stages. An LLM first detects and keeps source-language samples, then translates them while preserving named entities and code. Finally, the output is checked for leftover foreign-language text, compared with the original using embedding similarity, and scored by an LLM with chain-of-thought prompting on several quality criteria. Samples below a threshold are dropped, which keeps human effort low. The authors benchmarked 18 open-source models on the result. Larger RoPE-based models, especially instruction-tuned ones, generally score higher than APE (BERT-style) encoders. The benchmark is merged into the MTEB framework, giving Vietnamese a broader and more standardized testbed for choosing embedding models in retrieval, RAG and moderation systems.

## Key points
- VN-MTEB contains 41 datasets over six tasks: retrieval (15), classification, pair classification (3), clustering (5), reranking (3) and STS (3). It is released as an extension of MMTEB and integrated into MTEB.
- Filtering discards many samples. Average kept ratios are about 72% for clustering, 66% for retrieval and only 53.4% for STS, and some datasets shrink by nearly half.
- Across the 18 models evaluated, scores rise with parameter count. RoPE models such as e5-Mistral-7B-Instruct and e5-Qwen2-7B-Instruct generally beat APE models such as gte-multilingual-base, bge-m3 and m-e5-large.
- Instruction tuning helps: m-e5-large-instruct clearly outperforms m-e5-large. Higher embedding dimensionality tends to improve retrieval results.
- Stated limitations include no regeneration of rejected samples, limited handling of cultural context and dialect, no very long documents, and little analysis of synthetic-data bias.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Vietnam National University Ho Chi Minh City](https://md.research.elamur.ai/free/en/universities/vietnam-national-university-ho-chi-minh-city/) — University, Ho Chi Minh City, Vietnam · 21 works · 14 citations

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## Cite

Cite as: Loc Pham, Tung Luu, Thu Vo, Minh Nguyen and Viet Hoang. 2026. VN-MTEB: Vietnamese Massive Text Embedding Benchmark. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pham2026vn,
  title = {{VN-MTEB: Vietnamese Massive Text Embedding Benchmark}},
  author = {Loc Pham, Tung Luu, Thu Vo, Minh Nguyen and Viet Hoang},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.86},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.86}
}
```

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## References

28 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Jon Ander Campos, Yi Chern Tan, Kelly Marchisio, Max Bartolo, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Aidan Gomez, Phil Blunsom, Marzieh Fadaee, and 2 others.… — [Aya 23: Open Weight Releases to Further Multilingual Progress](https://md.research.elamur.ai/free/en/papers/aryabumi-2024-aya-23-open-weight-releases/)
2. Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, and 29 others. 2023. [Qwen technical report](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/). Preprint, arXiv:2309.16609.
3. Hongliu Cao. 2024. Recent advances in text embedding: A comprehensive review of top-performing methods on the mteb benchmark. Preprint, arXiv:2406.01607. — [arXiv:2406.01607](https://arxiv.org/abs/2406.01607) [find](https://scholar.google.com/scholar?q=Hongliu+Cao%2E+2024%2E+Recent+advances+in+text+embedding%3A+A+comprehensive+review+of+top%2Dperforming+methods+on+the+mteb+benchmark%2E+Preprint%2C+arXiv%3A2406%2E01607%2E+%E2%80%94+%5BarXiv%3A2406%2E01607%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F240)
4. Seamless Communication, Loı̈c Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean M… — [Seamless: Multilingual Expressive and Streaming Speech Translation](https://md.research.elamur.ai/free/en/papers/communication-2023-seamless-multilingual-expressive-streaming-speech/)
5. Alexis Conneau and Douwe Kiela. 2018. [SentEval: An evaluation toolkit for universal sentence representations](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/). In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
6. DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 181 others. 2025. [Deepseek-r1: Incentivizing reasoning capability in ll…](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/)
7. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
8. Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, Márton Kardos, Ashwin Mathur, David Stap, Jay Gala, Wissam Siblini, Dominik Krzemiński, Genta Indra Winata, Saba Sturua, Saiteja Utpala, Mathieu Ciancone, Marion Schaeffer, Diganta Misra, Shreeya Dhakal, Jonathan Rystrøm, Roman Solomatin, Ömer Veysel… — [MMTEB: Massive Multilingual Text Embedding Benchmark](https://md.research.elamur.ai/free/en/papers/enevoldsen-2025-mmteb-massive-multilingual-text-embedding/)
9. Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin. 2020. Beyond english-centric multiling… [find](https://scholar.google.com/scholar?q=Angela+Fan%2C+Shruti+Bhosale%2C+Holger+Schwenk%2C+Zhiyi+Ma%2C+Ahmed+El%2DKishky%2C+Siddharth+Goyal%2C+Mandeep+Baines%2C+Onur+Celebi%2C+Guillaume+Wenzek%2C+Vishrav+Chaudhary%2C+Naman+Goyal%2C+Tom+Birch%2C+Vitaliy+Liptchinsky%2C+S)
10. Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2022. [Language-agnostic BERT sentence embedding](https://md.research.elamur.ai/free/en/papers/feng-2020-language-agnostic-bert-sentence-embedding/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 878–891, Dublin, Ireland. Association for Computation…
11. Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, and 5 others.… [find](https://scholar.google.com/scholar?q=Leo+Gao%2C+Jonathan+Tow%2C+Baber+Abbasi%2C+Stella+Biderman%2C+Sid+Black%2C+Anthony+DiPofi%2C+Charles+Foster%2C+Laurence+Golding%2C+Jeffrey+Hsu%2C+Alain+Le+Noac%E2%80%99h%2C+Haonan+Li%2C+Kyle+McDonell%2C+Niklas+Muennighoff%2C+Chris+Oci)
12. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 o… — [The Llama 3 Herd of Models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
13. Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 427–431, Valencia, Spain. Associatio… [find](https://scholar.google.com/scholar?q=Armand+Joulin%2C+Edouard+Grave%2C+Piotr+Bojanowski%2C+and+Tomas+Mikolov%2E+2017%2E+Bag+of+tricks+for+efficient+text+classification%2E+In+Proceedings+of+the+15th+Conference+of+the+European+Chapter+of+the+Associati)
14. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Preprint, arXiv:2005.11401.
15. Niklas Muennighoff, Nouamane Tazi, Loic Magne, and Nils Reimers. 2023. [MTEB: Massive text embedding benchmark](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/). In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2014–2037, Dubrovnik, Croatia. Association for Computational Linguistic…
16. Chinh Ngo, Trieu H Trinh, Long Phan, Hieu Tran, Tai Dang, Hieu Nguyen, Minh Nguyen, and Minh-Thang Luong. 2022. Mtet: Multi-domain translation for english and vietnamese. arXiv preprint arXiv:2210.05610. — [arXiv:2210.05610](https://arxiv.org/abs/2210.05610) [find](https://scholar.google.com/scholar?q=Chinh+Ngo%2C+Trieu+H+Trinh%2C+Long+Phan%2C+Hieu+Tran%2C+Tai+Dang%2C+Hieu+Nguyen%2C+Minh+Nguyen%2C+and+Minh%2DThang+Luong%2E+2022%2E+Mtet%3A+Multi%2Ddomain+translation+for+english+and+vietnamese%2E+arXiv+preprint+arXiv%3A2210%2E056)
17. Kiet Nguyen, Vu Nguyen, Anh Nguyen, and Ngan Nguyen. 2020. A Vietnamese dataset for evaluating machine reading comprehension. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2595–2605, Barcelona, Spain (Online). International Committee on Computational Linguis… [find](https://scholar.google.com/scholar?q=Kiet+Nguyen%2C+Vu+Nguyen%2C+Anh+Nguyen%2C+and+Ngan+Nguyen%2E+2020%2E+A+Vietnamese+dataset+for+evaluating+machine+reading+comprehension%2E+In+Proceedings+of+the+28th+International+Conference+on+Computational+Lingu)
18. Kiet Van Nguyen, Vu Duc Nguyen, Phu X. V. Nguyen, Tham T. H. Truong, and Ngan Luu-Thuy Nguyen. 2018. Uit-vsfc: Vietnamese students’ feedback corpus for sentiment analysis. In 2018 10th International Conference on Knowledge and Systems Engineering (KSE), pages 19–24. [find](https://scholar.google.com/scholar?q=Kiet+Van+Nguyen%2C+Vu+Duc+Nguyen%2C+Phu+X%2E+V%2E+Nguyen%2C+Tham+T%2E+H%2E+Truong%2C+and+Ngan+Luu%2DThuy+Nguyen%2E+2018%2E+Uit%2Dvsfc%3A+Vietnamese+students%E2%80%99+feedback+corpus+for+sentiment+analysis%2E+In+2018+10th+International+C)
19. Thien Hai Nguyen, Tuan-Duy H. Nguyen, Duy Phung, Duy Tran-Cong Nguyen, Hieu Minh Tran, Manh Luong, Tin Duy Vo, Hung Hai Bui, Dinh Phung, and Dat Quoc Nguyen. 2022. A Vietnamese-English Neural Machine Translation System. In Proceedings of the 23rd Annual Conference of the International Speech Communi… [find](https://scholar.google.com/scholar?q=Thien+Hai+Nguyen%2C+Tuan%2DDuy+H%2E+Nguyen%2C+Duy+Phung%2C+Duy+Tran%2DCong+Nguyen%2C+Hieu+Minh+Tran%2C+Manh+Luong%2C+Tin+Duy+Vo%2C+Hung+Hai+Bui%2C+Dinh+Phung%2C+and+Dat+Quoc+Nguyen%2E+2022%2E+A+Vietnamese%2DEnglish+Neural+Machine+)
20. Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, and Fred Blain. 2024. What do large language models need for machine translation evaluation? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 3660… [find](https://scholar.google.com/scholar?q=Shenbin+Qian%2C+Archchana+Sindhujan%2C+Minnie+Kabra%2C+Diptesh+Kanojia%2C+Constantin+Orasan%2C+Tharindu+Ranasinghe%2C+and+Fred+Blain%2E+2024%2E+What+do+large+language+models+need+for+machine+translation+evaluation%3F+I)
21. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, and et al Adrià Garriga-Alonso. 2023. [Beyond the imitation game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/). Transactions on Machine Learn…
22. Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. 2023. [Roformer: Enhanced transformer with rotary position embedding](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/). Preprint, arXiv:2104.09864.
23. Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penche… [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Aishwarya+Kamath%2C+Johan+Ferret%2C+Shreya+Pathak%2C+Nino+Vieillard%2C+Ramona+Merhej%2C+Sarah+Perrin%2C+Tatiana+Matejovicova%2C+Alexandre+Ram%C3%A9%2C+Morgane+Rivi%C3%A8re%2C+Louis+Rouillard%2C+Thomas+Mesnard%2C+Geoffrey)
24. NLLB Team, Marta 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, and 20… [find](https://scholar.google.com/scholar?q=NLLB+Team%2C+Marta+R%2E+Costa%2Djuss%C3%A0%2C+James+Cross%2C+Onur+C%CC%A7elebi%2C+Maha+Elbayad%2C+Kenneth+Heafield%2C+Kevin+Heffernan%2C+Elahe+Kalbassi%2C+Janice+Lam%2C+Daniel+Licht%2C+Jean+Maillard%2C+Anna+Sun%2C+Skyler+Wang%2C+Guillaume+W)
25. Nandan Thakur, Nils Reimers, Andreas Ruckl’e, Abhishek Srivastava, and Iryna Gurevych. 2021. [Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/). ArXiv, abs/2104.08663.
26. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. [Llama: Open and efficient foundation language models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/). Preprint…
27. Kiet Van Nguyen, Khiem Vinh Tran, Son T Luu, Anh Gia-Tuan Nguyen, and Ngan Luu-Thuy Nguyen. 2020. Enhancing lexical-based approach with external knowledge for vietnamese multiple-choice machine reading comprehension. IEEE Access, 8:201404–201417. [find](https://scholar.google.com/scholar?q=Kiet+Van+Nguyen%2C+Khiem+Vinh+Tran%2C+Son+T+Luu%2C+Anh+Gia%2DTuan+Nguyen%2C+and+Ngan+Luu%2DThuy+Nguyen%2E+2020%2E+Enhancing+lexical%2Dbased+approach+with+external+knowledge+for+vietnamese+multiple%2Dchoice+machine+readin)
28. Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. [SuperGLUE: a stickier benchmark for general-purpose language understanding systems](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/). Curran Associates Inc., Red Hook, NY, USA. retrieval. In Proceedings of the 2024 Confe…

*Updated 31 October 2026*