GPT is the family of AI models behind many popular generative AI applications, such as chatbots and coding assistants. This article provides an overview of this game-changing innovation.
Table of contents
- What is GPT?
- How do GPT models work?
- How GPT models have evolved
- GPT applications
- Pros of GPT models
- Cons of GPT models
- Conclusion
What is GPT?
GPT, which stands for “generative pre-trained transformer,” refers to both a specific model and a family of progressively more sophisticated artificial intelligence (AI) models. Starting with the original GPT, the model has evolved through several versions, including the GPT-2, GPT-3, and GPT-4, each iteration expanding in size and capability and possessing an increased ability to handle complex language tasks with humanlike skill. The GPT family of models was developed by OpenAI, an AI research company founded in 2015 by a group of AI experts and backed by well-known founders such as Elon Musk and Reid Hoffman.
The GPT model is the foundation for numerous popular generative AI applications, including ChatGPT and DALL-E. GPT models are a type of large language model (LLM), which are designed to process and analyze extensive volumes of text data. LLMs are trained to proficiently mimic and generate humanlike language, enabling them to perform various tasks that require natural language understanding and generation.
What does GPT stand for?
GPT stands for “generative pre-trained transformer,” a description that encapsulates the essence of how it functions.
Generative
GPT models are called “generative AI” because they generate new content from prompts or input data. This sets them apart from AI models designed to classify and make predictions on existing, predefined data inputs. In contrast, generative AI models like GPT do not just classify data. Instead, they produce entirely new text outputs, code, images, or other creative media as a function of their training.
Pre-trained
Before being tailored to a specific application, GPT models undergo an initial pre-training phase. Pre-training establishes the model’s foundational ability to generate humanlike responses from arbitrary prompts by training the model on a well-curated dataset. This lays the groundwork for the model’s general language understanding capabilities.
Once the base pre-training is complete, developers can fine-tune the model for more specialized purposes through additional training on task-specific data. For example, a pre-trained GPT model can be fine-tuned on conversational datasets to function as a chatbot. Alternatively, it could be fine-tuned on specific codebases or documentation to assist with programming and code generation tasks. The pre-training provides the general language skills that can be refined to optimize the model for targeted use cases.
Transformer
Well-known AI architectures like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks process text sequences incrementally, making it difficult to capture full context and complex word structures. The transformer revolutionized natural language processing (NLP) with self-attention mechanisms that analyze all words in a sequence in parallel and build connections based on identified relationships.
By holistically processing entire sequences rather than individual words, transformers can grasp complex language structures far better than other architectures. However, a transformer’s “understanding” is actually just statistical patterns and is not humanlike comprehension or reasoning.
First introduced for machine translation in 2017, the transformer’s self-attention capabilities were a breakthrough, enabling training on massive datasets. As such, the transformer architecture now underpins most modern generative AI platforms as the standard architectural component.
From prompt to response—how GPT models work
GPT models work by forecasting the appropriate response to a given user input, referred to as a prompt. Originally, these models primarily interacted through text-based prompts, but advancements have introduced the capability to process uploaded documents and images, as well as access APIs and external tools for input data.
GPT models break prompts into smaller segments known as tokens and then analyze these tokens using sophisticated algorithms. This process aids in deciphering the tokens’ meanings within the prompt. Once the meaning has been extracted, the models generate responses that are statistically most likely to align with the expected answer.
How GPT models are trained
While the training processes for each GPT model vary, you can generally categorize them into two phases: unsupervised and supervised.
Unsupervised training
During the initial pre-training phase, GPT models ingest massive amounts of unlabeled data from varied sources like Wikipedia articles, digital books, and online discussions. For example, GPT-2 was trained on 8 million web pages, while the latest GPT-4 reportedly used a petabyte of text data, equivalent to 500 billion book pages. The goal of this self-supervised pre-training, referred to as the unsupervised phase, is to enable the model to comprehend natural language prompts and generate humanlike responses coherently. In this phase, the model isn’t explicitly told what the data represents. Instead, the model uses its transformer architecture to identify patterns and relationships in the data.
Supervised training
After the unsupervised phase is complete, GPT models are refined using supervised training. In supervised training, humans train the model using tailored, labeled prompts and responses with the goal of teaching the model which responses humans will likely want and which ones are harmful or inaccurate.
Supervised training also includes a process called reinforcement learning with human feedback (RLHF). In the RLHF process, humans rate responses to get the model to generate higher-quality responses over time.
During fine-tuning, GPT models may also be provided with specific types of data related to the function they will perform. For example, ChatGPT was fine-tuned on conversational dialogues and publicly available computer code to support its general ability to generate conversational text and accurate computer code.
How GPT models have evolved
Since 2018, OpenAI has released several versions of the GPT model, including GPT-2, GPT-3, and the most recent GPT-4, with each version building on the last to achieve greater complexity and capability in language processing tasks.
GPT-1
Introduced in 2018, GPT-1 demonstrated the potential of the GPT architecture and training approach. It was capable of basic language tasks like answering simple questions and rephrasing sentences. However, GPT-1 was best suited for shorter prompts and responses due to its smaller scale and simpler training dataset. These limitations caused it to struggle with maintaining context in longer conversations, often leading to less coherent outputs as the text length increased.
GPT-2
Launched in February 2019, GPT-2 represented a significant upgrade, as it was trained on a dataset ten times larger than that of GPT-1. This expanded training base allowed GPT-2 to generate longer, more coherent text and handle tasks like text summarization, question answering, and language translation without task-specific training. Despite these advances, GPT-2 still faced challenges with nuanced context understanding and occasionally produced responses that lacked relevance or strayed from user intentions.
GPT-3 and GPT-3.5
Released in June 2020, GPT-3 marked a significant advance from previous models, boasting improved abilities in natural language processing, code generation, and basic reasoning tasks like unscrambling sentences. With its massive scale of 175 billion parameters, GPT-3 greatly improved context retention and coherence over longer text spans. However, its larger size also introduced challenges in computational demands and fine-tuning, occasionally leading to unpredictable or biased outputs.
In 2022, OpenAI rolled out GPT-3.5, a refined version of GPT-3. By training on a more recent dataset and through additional fine-tuning, this version was designed to reduce the likelihood of generating harmful or inappropriate responses. While GPT-3.5 continued to advance in accuracy and safety, maintaining contextual accuracy in complex or niche contexts remained a challenge.
GPT-4
In March 2023, OpenAI released GPT-4, providing limited details about its training. With its ability to process longer and more complex prompts and significantly improved context retention, GPT-4 marks a considerable progression in GPT architecture. GPT-4 is also a multimodal model, which means that it can interpret prompts that include both text and images. While GPT-4 offers enhanced accuracy and functionality, it continues to face challenges with ensuring consistent reliability across diverse and nuanced tasks.
GPT applications
GPT models offer functionality that enables both nontechnical users and developers to tackle a broad range of tasks, including generating creative content, analyzing complex documents, and streamlining customer service.
Chatbots
Chatbots are among the most popular applications of GPT models. Using fine-tuning, developers can further customize GPT models to create specialized chatbots for specific purposes, such as providing customer service for businesses or teaching card games like poker. This customization supports engaging and contextually relevant interactions, creating a more personalized and helpful user experience.
Creative tasks
GPT models can support a variety of creative tasks, such as brainstorming or providing ideas for improving existing content. Here are some ways GPT models can help you with creative tasks:
- Writing drafts of original content, such as fiction, poetry, or advertising
- Generating ideas for creative endeavors like film script outlines or themes for a mural
- Suggesting ways to make existing content easier to read or more appealing to different audiences
Many generative AI tools allow you to generate creative content, including Grammarly. Grammarly learns your writing style and easily integrates with familiar tools, such as Gmail and Microsoft Word.
Academic support
GPT models can be applied in academic settings to help explain complex mathematical concepts, create engaging instructional content, serve as research assistants, and develop quizzes and exam questions.
Data analysis
While all GPT models can assist with data analysis tasks, GPT-4, in particular, excels at analyzing complex documents, summarizing data trends, and reporting metrics from structured data sources like Microsoft Excel documents. It can also analyze customer sentiment from social media comments, reviews, and surveys.
Image analysis
With GPT-4, users can upload images for analysis along with textual prompts. This feature is useful for a wide variety of tasks, such as converting images of text into editable formats, creating captions for social media posts, drafting product descriptions, and creating image descriptions for use with assistive technologies for visually impaired users.
Coding assistance
GPT models can assist developers by explaining a computer program, optimizing code for efficiency and maintainability, creating test cases, and converting code between programming languages. These capabilities help streamline the development process.
What are the pros of GPT models?
GPT models provide flexible and efficient ways to automate tasks, with support for significant customization. They allow users to create applications tailored to varied needs, such as contract analysis, predictive analytics, and cybersecurity threat detection. This adaptability has facilitated the broader adoption of AI across various sectors.
What are the cons of GPT models?
Despite their sophistication, GPT models have limitations. Because they are trained on fixed datasets, usually with a cutoff date, they can’t incorporate real-time updates or data after their last training cutoff. Additionally, while GPT-4 can analyze images, GPT models are text-based, so GPT-4 actually uses another generative AI model, DALL-E, to analyze and generate images. While this may not concern the average user, developers may find that natively multimodal models better serve their use cases. Lastly, ethical concerns persist around potential biases, privacy issues, and the possibility of misuse through, for example, spreading misinformation, infringing on copyright protections, or generating dangerous content.
GPT: An AI game changer
The GPT series of AI models have significantly advanced the capabilities of machines in mimicking human-like interactions and aiding in intricate tasks across multiple sectors. With their ongoing evolution, these models promise to enhance both creative and analytical endeavors. Nevertheless, they bring forth significant ethical and privacy concerns that necessitate diligent study and action. Looking ahead, the development of GPT technology will likely continue to be a central theme in AI research, shaping the future of technological adoption worldwide.
