are transformer based models part of semantic language model or vice versa ?

Question

I am confused as the core fundamentals are same that the tokens with the similar semantic meaning are placed closer on the multi-dimensional coordinates.

This question is related to the following Learning Module

Answer 1

Transformers are a type of neural network architecture that excels at understanding and processing sequences of words by focusing on important parts of the input, which helps them handle long sentences and complex language structures efficiently. Examples of these models include BERT and GPT, which are used for tasks like translation and text generation.

Semantic language models aim to understand and represent the meaning of words and sentences, placing similar meanings close together in a conceptual space. While transformers are often used to build these semantic models because of their powerful capabilities, semantic modeling can use various methods. In essence, transformers are a tool that helps achieve the goal of semantic understanding in language models.

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hth

Marcin