most citedMMTEB: Massive Multilingual Text Embedding Benchmark

15 citations · 17 across the 9 of their papers we have counts for

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cs.CL2026

RUMBA: Russian User Memory Benchmark

Elizaveta Shevtsova, Inna Glebkina, Mark Baushenko +2

The ability to handle long-term memory in LLMs is becoming increasingly critical, yet existing benchmarks remain English-centric and rely on aggregate retrieval metrics, failing to…

cs.CL2025★ 1 cited

Multimodal Evaluation of Russian-language Architectures

Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov +15

Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, a…

cs.CL2025

DRAGOn: Designing RAG On Periodically Updated Corpus

Fedor Chernogorskii, Sergei Averkiev, Liliya Kudraleeva +4

This paper introduces DRAGOn, method to design a RAG benchmark on a regularly updated corpus. It features recent reference datasets, a question generation framework, an automatic e…

cs.CL2025

Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX

Nikita Martynov, Anastasia Mordasheva, Dmitriy Gorbetskiy +8

We introduce POLLUX, a comprehensive open-source benchmark designed to evaluate the generative capabilities of large language models (LLMs) in Russian. Our main contribution is a n…

cs.CL2025

REPA: Russian Error Types Annotation for Evaluating Text Generation and Judgment Capabilities

Alexander Pugachev, Alena Fenogenova, Vladislav Mikhailov +1

Recent advances in large language models (LLMs) have introduced the novel paradigm of using LLMs as judges, where an LLM evaluates and scores the outputs of another LLM, which ofte…

cs.CL2025★ 15 cited

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83

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 co…