GPT-1, GPT-2 and GPT-3 models explained

Artificial Intelligence is the foundation of all human activity and the pursuit of intelligent machines has become an important endeavor of humanity. There have been some incredible technological advances in the field of Artificial Intelligence in this century. These revolutionary discoveries are a result of decades of research and trial and error. Today, the concept of computational linguistics and computer science is changing many aspects of businesses and our life. With Natural Language processing technology, machines can learn the text and make sense of Human Language. It has opened a new chapter in Machine Learning.

In 2020, OpenAI known as an organization dedicated to “discovering and enacting the path to safe artificial Intelligence” announced the arrival of the latest natural language processing technology known as GPT-3 (Generative Pre-trained Transformer). It is defined as a super-intelligent system that learns and adopts from the vast sea of digital text to generate new, intelligent and creative content on its own. It has been described as a remarkable AI text generator capable of mimicking human writing with great fluency. Its capabilities are termed as “AI that is too dangerous to be released publicly”. GPT models have transformed the Natural language processing (NLP) landscape with their powerful capabilities in performing various NLP tasks. The results are swift response time and greater accuracy. These language models require very less or even no examples to understand the task and perform with an even better precision and creativity compared to the state-of-the-art models which are trained heavily on a large set of examples.

As suggested in the name, GPT-3 is the third in a series of NLP tools designed by OpenAI. Before its launch, the model has taken years of development and has its journey to reach the state of innovation as we know it today within the field of AI text generation. This article will discuss the journey and evolution of GPT models i.e.: GPT-1, GPT-2, and GPT-3.

Before GPT, NLP models were heavily trained on large amounts of annotated data for a particular task. This caused a major limitation as the amount of labeled data needed to train the model accurately was not easily available. The NLP models were limited to what they have been trained for and failed to perform out-of-the-box tasks. To overcome these limitations OpenAI proposed a Generative Language Model (GPT-1) built using unlabeled data and then allowing users to fine-tune the language model so that it can perform downstream tasks such as classification, question answering, sentiment analysis, etc. This means that the model takes input (a sentence/a question) and tries to generate an appropriate response, and the data used for training the model is not labeled.

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GPT-1 was launched in 2018 by OpenAI. Trained on an enormous BooksCorpus dataset, this generative language model was able to learn large range dependencies and acquire vast knowledge on a diverse corpus of contiguous text and long stretches. In terms of its architecture GPT-1 applies the 12-layer decoder of the transformer architecture with a self-attention mechanism for training. As a result of its pre-training, one of the significant achievements of GPT-1 was its ability to carry out zero-shot performance on various tasks. This ability proved that generative language modeling can be exploited with an effective pretraining concept to generalize the model. With Transfer learning as its base GPT became a powerful facilitator to perform natural language processing tasks with very little fine-tuning. It generated pathways for other models which could further enhance its potential in generative pre-training with larger datasets and parameters.

Figure 1 GPT uses the Decoder part of the Transformer Model (Source: Attention is all you need)

Later in 2019, OpenAI developed a Generative pre-trained Transformer 2 (GPT-2) using a larger dataset and adding additional parameters to build a stronger language model. Similar to GPT-1, GPT-2 leverages the decoder of the transformer model. Some of the significant developments in GPT-2 is its model architecture and implementation, with 1.5 billion parameters it became 10 times larger than GPT-1 (117 million parameters), also it has 10 times more parameters and 10 times the data compared to its predecessor GPT-1. It is trained upon a diverse dataset making it powerful in terms of solving various language tasks related to translation, summarization, etc. by just using the raw text as input and taking few or no examples of training data. GPT-2 evaluation upon several datasets of downstream tasks, showed that it outperformed by improving the accuracy significantly in identifying long-range dependencies and predicting sentences.

Figure 1 GPT uses the Decoder part of the Transformer Model (Source: Attention is all you need)

GPT-3 is the third version of the Generative pre-training Model series so far. It is a massive language prediction and generation model developed by OpenAI capable of generating long sequences of the original text. GPT-3 became the OpenAI’s breakthrough AI language program. In simple words, it is a software application that can automatically generate paragraphs so unique that it almost sounds as if a person wrote them. GPT-3 program is currently available with restricted access through an API on the cloud, and access is needed to explore the tool. It has created some intriguing applications since its launch. Its significant benefit is its size, it contains about 175 billion parameters and is 100 times larger than GPT-2. It is trained upon a 500-billion-word data set (known as “Common Crawl”) collected from the vast internet and content repository. Its other significant and surprising ability is to perform simple arithmetic problems, including writing code snippets and execute intelligent tasks. The results are faster response time and accuracy allowing NLP models to benefit business by effectively and consistently maintaining best practices and reducing human errors. Many researchers and developers have described it as the ultimate black box AI approach due to its complexity and enormous size. This makes it a lot expensive and inconvenient to perform inference, also its billion-parameter size makes it heavy on resources and a challenge for practical applicability on tasks in its current form. It is currently available as an API through an application process interface provided by OpenAI.

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The purpose of GPT-3 was to make language processing more powerful and faster than its previous versions and without any special tuning. Most of the previous language processing models (such as BERT) require in-depth fine-tuning with thousands of examples to teach the model how to perform downstream tasks. With GPT-3 users can eliminate the fine-tuning step. The difference between the three GPT models is their size. The original Transformer Model had around 110 million parameters. GPT-1 adopted the size and with GPT-2 the number of parameters was enhanced to 1.5 billion. With GPT-3, the number of parameters was boosted to 175 billion, making it the largest neural network.

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Figure 1 GPT uses the Decoder part of the Transformer Model (Source: Attention is all you need)

GPT language models are a gigantic neural network with a great potential for automating tasks. Since NLP is an active area of research, it comes with its limitations that include repetitive text, misunderstanding of contextual phrases or technical restrictions, etc. Language processing in itself is a complex domain that requires intensive and excessive training and exposure to not just words but their context and meanings, these include: How sentences are formed, how answers are generated that are meaningful and appropriate to their context? etc. GPT-3 is capable of responding to any text by generating a new piece of text that is both creative and appropriate to its context. Their capabilities can amplify human efforts in a wide range of tasks from question and answers, customer service, document searches, report generation, content or code generation, and many more, making it an AI tool with great potential. With businesses and researchers focusing their efforts to create value with AI language technology, it will be interesting to see what the next discovery can produce.

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