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 …

Transformer language models (LMs) are fundamental to NLP research methodologies and
applications in various languages. However, developing such models specifically for the …

In this paper, we introduce an advanced Russian general language understanding evaluation
benchmark -- RussianGLUE. Recent advances in the field of universal language models …

This paper studies the generation methods for paraphrasing in the Russian language. There
are several transformer-based models (Russian and multilingual) trained on a collected …

This paper introduces mGPT, a multilingual variant of GPT-3, pretrained on 61 languages
from 25 linguistically diverse language families using Wikipedia and the C4 Corpus. We detail …

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 includes texts …

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 …

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 …

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 …

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' …