49 citations · 135 across the 12 of their papers we have counts for
13 papers
WebPageBench: Event-Level Verification and Controlled UI-Variant Generation for Web Agents
Anton Emelyanov, Maria Tikhonova, Zaven Martirosian +2
We present WebPageBench, an open framework for evaluating web agents in which every task is verified from the interface's own event log. Six instrumented mock sites with brand iden…
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…
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…
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…
Long Input Benchmark for Russian Analysis
Igor Churin, Murat Apishev, Maria Tikhonova +5
Recent advancements in Natural Language Processing (NLP) have fostered the development of Large Language Models (LLMs) that can solve an immense variety of tasks. One of the key as…
The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design
Artem Snegirev, Maria Tikhonova, Anna Maksimova +2
Embedding models play a crucial role in Natural Language Processing (NLP) by creating text embeddings used in various tasks such as information retrieval and assessing semantic tex…
MERA: A Comprehensive LLM Evaluation in Russian
Alena Fenogenova, Artem Chervyakov, Nikita Martynov +16
Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). As the models' siz…
A Family of Pretrained Transformer Language Models for Russian
Dmitry Zmitrovich, Alexander Abramov, Andrey Kalmykov +10
Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian…
TAPE: Assessing Few-shot Russian Language Understanding
Ekaterina Taktasheva, Tatiana Shavrina, Alena Fenogenova +11
Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes. However, this fast-growing area lacks standardized evaluation…
mGPT: Few-Shot Learners Go Multilingual
Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova +3
Recent studies report that autoregressive language models can successfully solve many NLP tasks via zero- and few-shot learning paradigms, which opens up new possibilities for usin…
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models
Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov +6
In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of langua…
MOROCCO: Model Resource Comparison Framework
Valentin Malykh, Alexander Kukushkin, Ekaterina Artemova +3
The new generation of pre-trained NLP models push the SOTA to the new limits, but at the cost of computational resources, to the point that their use in real production environment…
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
Tatiana Shavrina, Alena Fenogenova, Anton Emelyanov +7
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE. Recent advances in the field of universal language models and tr…