M. Arabov

A striking task-dialect gap reveals that critical areas like summarization for Maghrebi, Iraqi, and Sudanese dialects remain largely unaddressed in Arabic NLP research.

The first holistic framework for a Tajik electronic dictionary could revolutionize NLP applications in low-resource languages.

Even state-of-the-art multilingual models struggle to tag parts-of-speech in Tajik when trained on isolated words, highlighting the critical role of syntactic context.

Unlock Tajik NLP: a new open-source toolkit delivers a comprehensive pipeline for processing Cyrillic-script Tajik text, complete with datasets and pre-trained embeddings.

Forget scaling laws: QLoRA-tuned Mistral 7B crushes other architectures for low-resource Tajik text generation, highlighting the importance of architecture choice in PEFT.