Comparison: GPT-1 vs. GPT-2 vs. GPT-3

GPT-3 (Generative Pre-trained Transformer 3) is the world's largest and most advanced language model for artificial text generation. It can logically finish a narrative, write extended essays on a given topic, compose poems, and do translations.

As you can guess, GPT-3 is the third such model developed by OpenAI. The models went through several iterations, and in this article, we will look at the path that led to the now-popular ChatGPT, which is well described in the next article.

Before GPT

Before the advent of GPT, NLP models were trained for a specific task. It led to limitations since the amount of labeled data needed to train the model was not easily accessible. NLP models were limited by what they were trained for and could not perform non-standard tasks. To overcome these limitations, OpenAI has proposed a generative language model (GPT-1) built on unlabeled data and allows users to fine-tune the language model to perform subsequent tasks such as classification and answering questions, mood analysis, etc. It means that the model receives input data (sentence/question) and tries to generate an appropriate answer, and the data used to train the model is not labeled.

GPT-1

GPT-1 was released in 2018. This language model was able to study several dependencies and had extensive knowledge on many topics.

It used a 12-layer decoder with transformer architecture and a self-learning mechanism. Also, one of the significant achievements was zero performance. This ability proved that generative language models could be used with a practical pre-learning concept to generalize the model.

Using transfer learning as a foundation, GPT-1 has become a powerful model for performing natural language processing tasks. It has created paths for other models that further enhance its potential in generative pre-learning with large datasets and parameters.

GPT-2

In 2019, OpenAI developed GPT-2, using an even larger dataset and adding additional parameters to build a more robust language model. Some significant changes in GPT-2 are the architecture and implementation of the model with 1.5 billion parameters. It has become ten times larger than GPT-1 (10 times more parameters and data).

It was trained on several data sets, which made it great in solving various language tasks related to translation, generalization, etc., using only raw text as input and taking few or no examples of training data.

The evaluation of GPT-2 on several data sets for subsequent tasks showed that it surpassed them, significantly increasing the accuracy in determining long-range dependencies and predicting sentences. However, GPT-2 could not cope well with text generalization. In the experiments, its performance was similar to or worse than that of classical models trained for generalization.

GPT-3

GPT-3 is the third and most recent version. It is a massive language prediction and generation model developed by OpenAI, capable of generating long sequences of the source text. It has become a breakthrough AI language program. It is currently available with limited access via the API.

Its significant advantage is its size: it contains about 175 billion parameters and is 100 times larger than its predecessor. It is trained on a dataset of 500 billion words, collected from the Internet and a content repository.

Its other significant ability is to perform simple arithmetic tasks, including writing code snippets and performing intellectual tasks. The result is faster response times and accuracy, which allows NLP models to benefit businesses by efficiently and consistently supporting best practices and reducing human error.

Many researchers and developers call this approach to AI a "black box" because of its complexity and size. Its size of a billion parameters makes it resource-intensive and difficult to use in practical tasks in its current form.

Currently, GPT-3 is available as an API through the application process interface provided by OpenAI.

Challenges

There are limitations that OpenAI had to take into account. These include repetitive text, misunderstanding of highly technical and specialized topics, and misunderstanding contextual phrases.

Language and linguistics is a complex and extensive field that, as a rule, requires many years of training to understand not only the meaning of words but also how to form sentences and give answers that have contextual meaning, as well as use the appropriate slang.

Although generative pre-trained transformers are a milestone in the artificial intelligence race, they are not adapted to work with complex, lengthy texts. If you imagine a sentence or paragraph containing words from very specialized fields, such as, for example, literature, finance, or medicine, the model will not be able to generate the appropriate answers without sufficient training. It also applies to different languages, so if you want to get answers in French, the model must be trained again.

Summary

Currently, this is not an acceptable solution for the masses due to the significant computing resources and power needed. Billions of parameters require a lot of computing resources to run and train. But in the future, models like GPT-3 will become less of a novelty and be used in various fields of our lives.