MMTEB: Massive Multilingual Text Embedding Benchmark
Authors: name: Kenneth Enevoldsen; name: Isaac Chung; name: Imene Kerboua; name: Márton Kardos; name: Ashwin Mathur; name: David Stap; name: Jay Gala; name: Wissam Siblini; name: Dominik Krzemiński; name: Genta Indra Winata; name: Saba Sturua; name: Saiteja Utpala; name: Mathieu Ciancone; name: Marion Schaeffer; name: Gabriel Sequeira; name: Diganta Misra; name: Shreeya Dhakal; name: Jonathan Rystrøm; name: Roman Solomatin; name: Ömer Çağatan; name: Akash Kundu; name: Martin Bernstorff; name: Shitao Xiao; name: Akshita Sukhlecha; name: Bhavish Pahwa; name: Rafał Poświata; name: Kranthi Kiran GV; name: Shawon Ashraf; name: Daniel Auras; name: Björn Plüster; name: Jan Philipp Harries; name: Loïc Magne; name: Isabelle Mohr; name: Mariya Hendriksen; name: Dawei Zhu; name: Hippolyte Gisserot-Boukhlef; name: Tom Aarsen; name: Jan Kostkan; name: Konrad Wojtasik; name: Taemin Lee; name: Marek Šuppa; name: Crystina Zhang; name: Roberta Rocca; name: Mohammed Hamdy; name: Andrianos Michail; name: John Yang; name: Manuel Faysse; name: Aleksei Vatolin; name: Nandan Thakur; name: Manan Dey; name: Dipam Vasani; name: Pranjal Chitale; name: Simone Tedeschi; name: Nguyen Tai; name: Artem Snegirev; name: Michael Günther; name: Mengzhou Xia; name: Weijia Shi; name: Xing Han Lù; name: Jordan Clive; name: Gayatri Krishnakumar; name: Anna Maksimova; name: Silvan Wehrli; name: Maria Tikhonova; name: Henil Panchal; name: Aleksandr Abramov; name: Malte Ostendorff; name: Zheng Liu; name: Simon Clematide; name: Lester James Miranda; name: Alena Fenogenova; name: Guangyu Song; name: Ruqiya Bin Safi; name: Wen-Ding Li; name: Alessia Borghini; name: Federico Cassano; name: Hongjin Su; name: Jimmy Lin; name: Howard Yen; name: Lasse Hansen; name: Sara Hooker; name: Chenghao Xiao; name: Vaibhav Adlakha; name: Orion Weller; name: Siva Reddy; name: Niklas Muennighoff
Published: 2025-02-19 10:13:43

Abstract: Text embeddings are typically evaluated on a limited set of tasks, which are
constrained by language, domain, and task diversity. To address these
limitations and provide a more comprehensive evaluation, we introduce the
Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale,
community-driven expansion of MTEB, covering over 500 quality-controlled
evaluation tasks across 250+ languages. MMTEB includes a diverse set of
challenging, novel tasks such as instruction following, long-document
retrieval, and code retrieval, representing the largest multilingual collection
of evaluation tasks for embedding models to date. Using this collection, we
develop several highly multilingual benchmarks, which we use to evaluate a
representative set of models. We find that while large language models (LLMs)
with billions of parameters can achieve state-of-the-art performance on certain
language subsets and task categories, the best-performing publicly available
model is multilingual-e5-large-instruct with only 560 million parameters. To
facilitate accessibility and reduce computational cost, we introduce a novel
downsampling method based on inter-task correlation, ensuring a diverse
selection while preserving relative model rankings. Furthermore, we optimize
tasks such as retrieval by sampling hard negatives, creating smaller but
effective splits. These optimizations allow us to introduce benchmarks that
drastically reduce computational demands. For instance, our newly introduced
zero-shot English benchmark maintains a ranking order similar to the full-scale
version but at a fraction of the computational cost.