Let us learn about the evolution of GPT models and key comparisons between these GTP models
Large language models’ introduction has allowed for significant advancement in the field of natural language processing during the past several years. Machine translation systems learn how to map strings from one language to another using language models. The Generative Pre-Trained Transformer (GPT) based Model has attracted the greatest interest lately within the family of language models. The language models were initially rule-based systems that were highly dependent on user input to operate. However, the complexity, size, and accuracy of the tasks performed by these models have improved as a result of the development of deep learning approaches.
Let’s turn our attention to the GPT Models and its pillars. We will also examine the evolution of GPT models, starting with GPT-1 and moving on to the newly released GPT-4, and explore the major advancements made in each generation that gave the models more strength over time.
Understanding GPT Models
A deep learning-based Large Language Model (LLM), GPT (Generative Pre-trained Transformers) has a decoder-only architecture based on transformers. Its goal is to process text data and produce writing that looks and sounds like human language.
The three pillars are explained below:
1. Generative
The model’s capacity to produce text by understanding and reacting to a given text sample is highlighted by this feature. Text output was previously created by rearranging or extracting words from the input itself before GPT models. The advantage that GPT models had over other models was their capacity to generate language that was more cohesive and human-like.
This generative capacity derives from the training’s modeling purpose.
The most appropriate next word is attempted to be determined by GPT models utilizing probability distributions to forecast the most likely word or phrase. Autoregressive language modeling is a technique used to train GPT models.
2. Pre-Trained
An ML model is referred regarded be “pre-trained” if it has been trained on a sizable dataset of samples before being used for a particular job. In the instance of GPT, the model is trained using an unsupervised learning strategy on a sizable corpus of text data. As a result, the model may discover patterns and connections in the data on its own.
To put it another way, the model learns the broad characteristics and structure of a language by being trained with a large quantity of unstructured data. Once mastered, the model may use this comprehension for specific tasks like summarising and answering questions.
3. Transformer
a specific kind of neural network architecture made to deal with text sequences of various lengths. After the ground-breaking study “Attention Is All You Need” was released in 2017, the idea of transformers sprang into popularity.
The GPT architecture is a decoder-only one. A transformer’s “self-attention mechanism,” which enables the model to capture the relationship between each word and other words in the same phrase, is its main functional component.
Evolution of GPT Models
Let’s now examine the GPT Models in more detail, paying particular attention to the improvements and additions made in each new iteration.
GPT-1
It was learned using about 40GB of text data and is the first model in the GPT series. For modeling jobs like LAMBADA, the model produced cutting-edge results, while for tasks like GLUE and SQuAD, it performed well. The model may save data for relatively short phrases or documents each request with a context length limit of 512 tokens (or around 380 words). The creation of the next model in the series was spurred on by the model’s outstanding text production skills and good performance on common tasks.
GPT-2
The GPT-2 Model is a descendant of the GPT-1 Model and shares the same architectural characteristics. In contrast to GPT-1, it is trained on an even bigger corpus of text data. Notably, GPT-2 can analyze larger text samples since it can handle input sizes that are twice as large. With around 1.5 billion characteristics, GPT-2 shows a notable improvement in capability and language modeling potential.
GPT-3
The GPT-3 Model is an improvement over the GPT-2 Model in several ways. It has a maximum of 175 billion parameters and was trained on a far bigger corpus of text data.
GPT-3.5
The GPT-3.5 series models were derived from the GPT-3 models, just as its forerunners. A method known as Reinforcement Learning with Human Feedback (RLHF) is used to add unique rules based on human values into GPT-3.5 models. This is what sets these models apart from other models. The main goals were to reduce toxicity, prioritize veracity in their created output, and better match the models with the user’s intent. To offer a safer and more dependable user experience, this evolution denotes an intentional attempt to improve the ethical and responsible employment of language models.
GPT-4
With multimodal features that enable it to handle both text and picture inputs while producing text outputs, GPT-4 is the newest model in the GPT series. It supports a variety of image types, including text-only documents, pictures, schematics, diagrams, graphs, and screenshots.
While OpenAI has not provided technical information on GPT-4, several estimates indicate that it has close to 1 trillion parameters. This information includes model size, architecture, training methods, and model weights. Similar to earlier GPT models, the primary goal of the GPT-4 base model is to predict the subsequent word given a series of words. During the training procedure, a sizable corpus of licensed and publicly accessible internet data were used.
In both internal adversarial factuality tests conducted by OpenAI and external benchmarks like TruthfulQA, GPT-4 has demonstrated performance advantages over GPT-3.5. The RLHF methods used in GPT-3.5 were carried over to GPT-4. GPT-4 is actively being improved by OpenAI based on input from ChatGPT and other sources.
