Portrait de Imene Kerboua

Imene Kerboua

Collaborateur·rice de recherche - INSA Lyon, France
Superviseur⋅e principal⋅e
Siva Reddy
Sujets de recherche
Agent basé sur un LLM
Site web
Google Scholar
GitHub

Publications

MMTEB: Massive Multilingual Text Embedding Benchmark
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
Gabriel Sequeira
Diganta Misra
Shreeya Dhakal
Jonathan Rystrøm
Roman Solomatin
Ömer Veysel Çağatan … (voir 66 de plus)
Akash Kundu
Martin Bernstorff
Shitao Xiao
Akshita Sukhlecha
Bhavish Pahwa
Rafał Poświata
Kranthi Kiran GV
Shawon Ashraf
Daniel Auras
Björn Plüster
Jan Philipp Harries
Loïc Magne
Isabelle Mohr
Mariya Hendriksen
Dawei Zhu
Hippolyte Gisserot-Boukhlef
Tom Aarsen
Jan Kostkan
Konrad Wojtasik
Taemin Lee
Marek Suppa
Crystina Zhang
Roberta Rocca
Mohammed Hamdy
Andrianos Michail
John Yang
Manuel Faysse
Aleksei Vatolin
Nandan Thakur
Manan Dey
Dipam Vasani
Pranjal A Chitale
Simone Tedeschi
Nguyen Tai
Artem Snegirev
Michael Günther
Mengzhou Xia
Weijia Shi
Xing Han Lu
Jordan Clive
Gayatri K
Maksimova Anna
Silvan Wehrli
Maria Tikhonova
Henil Shalin Panchal
Aleksandr Abramov
Malte Ostendorff
Zheng Liu
Simon Clematide
Lester James Validad Miranda
Alena Fenogenova
Guangyu Song
Ruqiya Bin Safi
Wen-Ding Li
Alessia Borghini
Federico Cassano
Hongjin Su
Jimmy Lin
Howard Yen
Lasse Hansen
Sara Hooker
Chenghao Xiao
Vaibhav Adlakha
Orion Weller
Siva Reddy
Niklas Muennighoff
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address… (voir plus) 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.
2025-02-19
ArXiv (prépublication)
doi.org
arxiv.org
MMTEB: Massive Multilingual Text Embedding Benchmark
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 Çağatan
Akash Kundu … (voir 62 de plus)
Martin Bernstorff
Shitao Xiao
Akshita Sukhlecha
Bhavish Pahwa
Rafał Poświata
Kranthi Kiran GV
Shawon Ashraf
Daniel Auras
Björn Plüster
Jan Philipp Harries
Loïc Magne
Isabelle Mohr
Dawei Zhu
Hippolyte Gisserot-Boukhlef
Tom Aarsen
Jan Kostkan
Konrad Wojtasik
Taemin Lee
Marek Suppa
Crystina Zhang
Roberta Rocca
Mohammed Hamdy
Andrianos Michail
John Yang
Manuel Faysse
Aleksei Vatolin
Nandan Thakur
Manan Dey
Dipam Vasani
Pranjal A Chitale
Simone Tedeschi
Nguyen Tai
Artem Snegirev
Mariya Hendriksen
Michael Günther
Mengzhou Xia
Weijia Shi
Xing Han Lu
Jordan Clive
Gayatri K
Maksimova Anna
Silvan Wehrli
Maria Tikhonova
Henil Shalin Panchal
Aleksandr Abramov
Malte Ostendorff
Zheng Liu
Simon Clematide
Lester James Validad Miranda
Alena Fenogenova
Guangyu Song
Ruqiya Bin Safi
Wen-Ding Li
Alessia Borghini
Federico Cassano
Lasse Hansen
Sara Hooker
Chenghao Xiao
Vaibhav Adlakha
Orion Weller
Siva Reddy
Niklas Muennighoff
Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this… (voir plus) limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-driven initiative expanding MTEB to over 500 quality-controlled evaluation tasks across 1,000+ languages. MMTEB includes a wide range of challenging novel tasks such as instruction following, long-document retrieval, and code retrieval, and represents the largest multilingual collection of evaluation tasks for embedding models to date. We use this collection to construct multiple highly multilingual benchmarks. We evaluate a representative set of models on these benchmarks. Our findings indicate that, while LLM-based models can achieve state-of-the-art performance on a subset of languages, the best-performing publicly available model across languages is the notably smaller, multilingual-e5-large-instruct. Massive benchmarks often impose high computational demands, limiting accessibility, particularly for low-resource communities. To address this, we downsample tasks based on inter-task correlation (i.e., selecting only a diverse set of tasks) while preserving relative rankings. We further optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks at a significantly lower computational cost. For instance, we introduce a new zero-shot English benchmark that maintains a similar ordering at a fraction of the cost.
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
MMTEB: Massive Multilingual Text Embedding Benchmark
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
Gabriel Sequeira
Diganta Misra
Shreeya Dhakal
Jonathan Rystrøm
Roman Solomatin
Ömer Veysel Çağatan … (voir 66 de plus)
Akash Kundu
Martin Bernstorff
Shitao Xiao
Akshita Sukhlecha
Bhavish Pahwa
Rafał Poświata
Kranthi Kiran GV
Shawon Ashraf
Daniel Auras
Björn Plüster
Jan Philipp Harries
Loïc Magne
Isabelle Mohr
Mariya Hendriksen
Dawei Zhu
Hippolyte Gisserot-Boukhlef
Tom Aarsen
Jan Kostkan
Konrad Wojtasik
Taemin Lee
Marek Suppa
Crystina Zhang
Roberta Rocca
Mohammed Hamdy
Andrianos Michail
John Yang
Manuel Faysse
Aleksei Vatolin
Nandan Thakur
Manan Dey
Dipam Vasani
Pranjal A Chitale
Simone Tedeschi
Nguyen Tai
Artem Snegirev
Michael Günther
Mengzhou Xia
Weijia Shi
Xing Han Lu
Jordan Clive
Gayatri K
Maksimova Anna
Silvan Wehrli
Maria Tikhonova
Henil Shalin Panchal
Aleksandr Abramov
Malte Ostendorff
Zheng Liu
Simon Clematide
Lester James Validad Miranda
Alena Fenogenova
Guangyu Song
Ruqiya Bin Safi
Wen-Ding Li
Alessia Borghini
Federico Cassano
Hongjin Su
Jimmy Lin
Howard Yen
Lasse Hansen
Sara Hooker
Chenghao Xiao
Vaibhav Adlakha
Orion Weller
Siva Reddy
Niklas Muennighoff
Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this… (voir plus) limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-driven initiative expanding MTEB to over 500 quality-controlled evaluation tasks across 1,000+ languages. MMTEB includes a wide range of challenging novel tasks such as instruction following, long-document retrieval, and code retrieval, and represents the largest multilingual collection of evaluation tasks for embedding models to date. We use this collection to construct multiple highly multilingual benchmarks. We evaluate a representative set of models on these benchmarks. Our findings indicate that, while LLM-based models can achieve state-of-the-art performance on a subset of languages, the best-performing publicly available model across languages is the notably smaller, multilingual-e5-large-instruct. Massive benchmarks often impose high computational demands, limiting accessibility, particularly for low-resource communities. To address this, we downsample tasks based on inter-task correlation (i.e., selecting only a diverse set of tasks) while preserving relative rankings. We further optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks at a significantly lower computational cost. For instance, we introduce a new zero-shot English benchmark that maintains a similar ordering at a fraction of the cost.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net