GPT Models: The Powerhouses of Language Generation in AI

GPT (Generative Pretrained Transformer) models are a class of natural language processing (NLP) models that use a large body of text as input, referred to as “corpus,” to generate language models. The language models are then used in downstream tasks such as natural language generation, sentiment analysis, question answering, and more.

At the core of GPT models is a transformer architecture. This architecture is a deep learning network that uses self-attention layers to process text, allowing GPT models to capture long-term dependencies. GPT models use a combination of recurrent and non-recurrent layers to process text, which lets them generate text that has a longer context than traditional recurrent neural network (RNN) models.

In order to generate a language model, GPT models use a Masked Language Modeling (MLM) process. In the MLM process, a subset of tokens in the original corpus is replaced with a special token denoting it is masked and left blank. The model’s task is to identify what replaces the masked token.

To train a GPT model, a common approach is to use a pretrained transformer model as the basis. This means that the model already has some knowledge of English words and their relationships. The pretrained model is then trained further with the text corpus given as input. During training, the model detects correlations within and between the words to discover patterns. This “learning” process helps the model learn to capture the context of words in new, unseen sentences.

When the model is finished training, it can now be used to generate text. To do this, the model takes input based on the text in the corpus. From this input, it generates new text based on its understanding of the patterns it learned while training. For example, the model can generate a sentence that follows traditional grammar rules and has the type of structure seen in the input corpus.

Overall, GPT models are powerful tools that can generate text on demand. They have shown excellent results in various natural language processing tasks. Furthermore, the fact that GPT models require a corpus of text as input means that the generated text is more semantically correct and less likely to generate nonsensical output.