  Multilingual Large Language Models (LLMs) have gained large popularity among
Natural Language Processing (NLP) researchers and practitioners. These models,
trained on huge datasets, show proficiency across various languages and
demonstrate effectiveness in numerous downstream tasks. This paper navigates
the landscape of multilingual LLMs, providing an introductory overview of their
technical aspects. It explains underlying architectures, objective functions,
pre-training data sources, and tokenization methods. This work explores the
unique features of different model types: encoder-only (mBERT, XLM-R),
decoder-only (XGLM, PALM, BLOOM, GPT-3), and encoder-decoder models (mT5,
mBART). Additionally, it addresses one of the significant limitations of
multilingual LLMs - the curse of multilinguality - and discusses current
attempts to overcome it.
