Foundations of GPT

 


GPT (Generative Pre-trained Transformer) models are a specific type of LLM (Language Model) that uses the Transformer architecture and pre-training techniques to generate text. LLMs, on the other hand, refer to a broader category of models that can perform various language-related tasks, such as text classification, question-answering, and machine translation. GPT models are just one type of LLM, and there are other types of LLMs that do not use the Transformer architecture or pre-training techniques. That said, the most used LLMs today are of the type GPT, so we want to describe the way GPT models work. This concept of generative AI has opened up new possibilities in various fields, such as natural language processing, computer vision, and machine learning. 

Generative

In this introduction, we will explore the concept of generative in GPT models.  Generative GPT models are capable of generating human-like text, images, and even code. The term "generative" refers to the ability of these models to create new content, rather than simply regurgitating existing data. The generative ability allows GPT models to produce text that is coherent and grammatically correct. This is achieved through autoregressive generation, where the model predicts the next word in a sequence based on the words that came before it. The model can generate text that is contextually relevant and flows naturally, similar to how humans write. 

 The statistical foundations of autoregressive generation are rooted in the concept of autoregressive (AR) models, which are a class of linear models used for modeling time series data. The parameters of an AR model used in autoregressive generation are typically estimated using Maximum Likelihood Estimation (MLE).  MLE helps the GPT model learn the statistical properties of the language by estimating the parameters of the model. 

The key aspects of autoregressive generation include the structure of AR models, the process of estimation, and the conditions for stationarity and invertibility. For an AR model to be useful for forecasting, it must be stationary. Invertibility refers to the ability to express the current value of the series as a linear combination of past errors. It ensures that the model is uniquely identifiable. 

TBD...

Attention Mechanisms

Another aspect of how generative works in GPT models is the use of attention mechanisms. These mechanisms allow the model to focus on specific parts of the input text and give more weight to relevant words and phrases. This helps the model to better understand the context and generate more accurate and coherent text.   TBD ... 

Additionally, GPT models also use a technique called positional encoding, which assigns a unique position to each word in a sequence, allowing the model to understand the order of words in a sentence. TBD ...

Pre-Trained 


GPT models are pre-trained on large amounts of data. TBD... 

Transformers 

GPT models are based on the Transformer architecture, which is a type of neural network that processes sequential data, such as text. TBD... 

Deep Learning 


Neural Networks 


Transfer Learning

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