49 citations · 144 across the 14 of their papers we have counts for
14 papers
GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture
GigaChat team, Mamedov Valentin, Evgenii Kosarev +31
Generative large language models (LLMs) have become crucial for modern NLP research and applications across various languages. However, the development of foundational models speci…
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…
RuBLiMP: Russian Benchmark of Linguistic Minimal Pairs
Ekaterina Taktasheva, Maxim Bazhukov, Kirill Koncha +3
Minimal pairs are a well-established approach to evaluating the grammatical knowledge of language models. However, existing resources for minimal pairs address a limited number of…
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 Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages
Nikita Martynov, Mark Baushenko, Anastasia Kozlova +3
Modern large language models demonstrate impressive capabilities in text generation and generalization. However, they often struggle with solving text editing tasks, particularly w…
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…
Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
Tatiana Shamardina, Vladislav Mikhailov, Daniil Chernianskii +7
We present the shared task on artificial text detection in Russian, which is organized as a part of the Dialogue Evaluation initiative, held in 2022. The shared task dataset includ…
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…
RuCLIP -- new models and experiments: a technical report
Alex Shonenkov, Andrey Kuznetsov, Denis Dimitrov +10
In the report we propose six new implementations of ruCLIP model trained on our 240M pairs. The accuracy results are compared with original CLIP model with Ru-En translation (OPUS-…
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…
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…
DaNetQA: a yes/no Question Answering Dataset for the Russian Language
Taisia Glushkova, Alexey Machnev, Alena Fenogenova +3
DaNetQA, a new question-answering corpus, follows (Clark et. al, 2019) design: it comprises natural yes/no questions. Each question is paired with a paragraph from Wikipedia and an…