This text presents a literature review of Language Models (LM), covering two main topics: (1) The Transformers-based Neural Network used to train modern language models; and (2) The Semantic Space produced by the network training of the LM, which computationaly represents the language being modeled. In fact, obtaining such a computational representation for textual constructs is a long-standing problem that has challenged diverse NLP (Natural Language Processing) approaches. The establishment of transformers-based language models opens up vast possibilities and perspectives on interdisciplinary topics beyond NLP. Therefore, this survey details the history, the development and the mechanisms of Transformers-based language models. The text concludes with a critical analysis addressing issues regarding applications based on language models.
