MMTEB: Massive Multilingual Text Embedding Benchmark
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.
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Niklas Muennighoff
MMTEB: Massive Multilingual Text Embedding Benchmark
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.
The MMTEB benchmark expands MTEB with over 500 tasks across 250+ languages to comprehensively evaluate text embeddings, finding that smaller multilingual models can outperform large LLMs in many cases.
text embeddings Massive Multilingual Text Embedding Benchmark (MMTEB) MTEB multilingual instruction following long-document retrieval code retrieval large language models (LLMs) multilingual-e5-large-instruct downsampling method inter-task correlation hard negatives zero-shot benchmark