Russian Language

Research on the Russian language is currently focused on bridging the gap between its rich linguistic features and the capabilities of modern natural language processing (NLP) tools. Key areas include developing and adapting large language models (LLMs), such as transformer-based architectures, for tasks like machine translation, text summarization, and grammatical error correction, often incorporating techniques like fine-tuning and adapter models to improve performance on Russian-specific challenges. This work is crucial for advancing NLP research in low-resource languages and has significant practical implications for applications ranging from improved speech recognition systems to more effective cross-cultural communication tools. Furthermore, efforts are underway to create and improve high-quality datasets for various NLP tasks in Russian and related languages, such as Chuvash and Ukrainian, to support further research and development.