What Is GPT?
GPT stands for Generative Pre-trained Transformer. It’s a series of AI language models developed by OpenAI that utilize deep learning techniques to produce human-like text. GPT can generate coherent and contextually relevant text based on a given prompt, making it useful for applications ranging from text completion to generating artistic content.
GPT models are based on the transformer architecture, an advanced neural network structure optimized for understanding and generating human language. This architecture leverages self-attention mechanisms that help the model consider the importance of words in a sentence, relative to each other, enhancing its predictive accuracy.
This is part of a series of articles about OpenAI GPT 4
What Is GPT 3?
GPT-3, the third iteration of the GPT series, can produce coherent and contextually relevant text based on minimal input. It was trained on a diverse range of Internet data, allowing it to mimic various writing styles and formats with accuracy.
With over 175 billion machine learning parameters, GPT-3 was the largest and most complex language model ever created up to its release. This vast number of parameters allows GPT-3 to understand and generate text with a level of sophistication that surpassed previous state of the art models, such as Microsoft’s Turing NLG model which contained 10 billion parameters.
What Is GPT 3.5?
GPT-3.5 introduced several improvements over GPT-3, focusing on enhancing the model’s ability to understand and generate text with greater accuracy and contextual relevance. One significant innovation is the fine-tuning process using Reinforcement Learning from Human Feedback (RLHF).
This technique involves training the model not only on vast datasets but also refining its responses based on direct human feedback, leading to more nuanced and accurate outputs. Additionally, GPT-3.5 features optimizations that improve its efficiency and reduce instances of generating incorrect, short, or nonsensical answers.
What Is GPT-4 Turbo?
GPT-4, launched in April 2023, was a large step forward in LLM technology compared to GPT-3.5, with over a trillion parameters and an innovative Mixture of Experts (MoE) architecture, combining multiple transformers to address user queries. It was the first model to achieve human-level performance on various cognitive benchmarks.
GPT-4 Turbo, launched in November 2023, was an update to the GPT-4 model, with significantly improved performance and efficiency. It offers faster response times and a more in-depth understanding of complex prompts. Key innovations included a more efficient transformer architecture and better utilization of computational resources, allowing for quicker and more accurate text generation.
What Is GPT4o?
GPT-4o, or GPT-4 Omni, released in May 2024, is the latest evolution in OpenAI’s large language model portfolio. It can process multimodal inputs, including both text and images, enabling a broader range of applications. The model introduces additional efficiency improvements—according to OpenAI it is 50% faster than GPT-4 Turbo while offering 50% lower API costs.
Most significantly, GPT-4o provides a new Voice Mode that enables users to interact with it using spoken language, in combination with inputs from a webcam or mobile phone camera. The model can respond in real time, with a latency similar to human conversation, and respond to both spoken prompts and visual inputs from the user’s camera. The model has also gained emotion and expressive vocal capabilities, making it sound much more human.
GPT 3 vs. GPT 4: Technical Differences
1. Model Size
GPT-3, with its 175 billion parameters, marked a significant leap in the scale of language models at its release. This extensive number of parameters allowed for a richer and more nuanced understanding of language, enabling GPT-3 to generate highly coherent and contextually relevant text. However, the massive model size also resulted in substantial computational resource requirements.
While the exact number of parameters in GPT-4 has not been disclosed, it is generally believed to have over 1 trillion parameters. This expansion enhances the model’s capacity to understand and generate even more complex and nuanced language, improving performance, accuracy, and contextual understanding. The increased model size, however, also necessitates more advanced computational resources for training and deployment.
2. Architectural Nuances
The architectural advancements from GPT-3 to GPT-4 include refined transformer mechanisms that improve efficiency and effectiveness in language processing. GPT-4 incorporates enhanced self-attention mechanisms, allowing it to better capture dependencies across longer text spans. This contributes to more accurate predictions and coherent text generation.
Additionally, GPT-4 uses Mixture of Experts (MoE), a more modular design that allows for greater flexibility in fine-tuning specific aspects of the model. This enables more targeted optimizations and adaptations, enhancing the model’s versatility.
3. Training Data
The training data for GPT-3 encompassed a diverse and extensive range of internet-based text, including websites, books, and other textual resources. This enabled GPT-3 to generate text in various styles and formats. However, the model’s reliance on such vast and unfiltered data sources posed challenges related to biases and the generation of inappropriate content.
GPT-4 addresses these issues by incorporating more curated and high-quality datasets during its training process. This improves the overall quality and relevance of the generated text and enhances the model’s ability to handle specific domains and contexts. GPT-4’s training process includes more sophisticated techniques for filtering and mitigating biases.
4. Context Window
GPT-3 originally offered a context window of 2,048 tokens (approx. 1000 words). This was the maximum amount of data the model could consider in a user prompt or during an ongoing conversation. GPT-3.5 initially increased the context window to 4,096 tokens, and later to 16K token with the upgrade to GPT-3.5 Turbo.
GPT-4 dramatically increased the context window to 32K tokens, and with the release of GPT-4 Turbo, the context window expanded to 128K tokens. This allows the model to read and analyze long documents and multimedia content such as images and video.
GPT 3 vs. GPT 4: Differences in Capabilities
5. Language Abilities
GPT-4 surpasses GPT-3 in understanding and generating nuanced language. While GPT-3 can produce coherent text, GPT-4 demonstrates a refined ability to interpret different dialects and emotional cues, making interactions feel more personalized and empathetic. GPT-4’s improved linguistic finesse enables it to better grasp and respond to subtleties in language, such as regional variations and emotional undertones.
6. Information Synthesis
One of the most significant advancements in GPT-4 is its ability to synthesize information from multiple sources to answer complex questions comprehensively. Unlike GPT-3, which may struggle with multi-faceted queries, GPT-4 can integrate data from various studies and resources, providing more thorough and nuanced responses. This makes GPT-4 particularly effective in contexts that require detailed and interconnected information, such as academic research or in-depth reporting.
7. Creativity
In terms of creative output, GPT-4 also outperforms GPT-3. While GPT-3 is capable of generating stories, poems, and essays, GPT-4 enhances this capability by producing more coherent and imaginative content. GPT-4 can craft narratives with well-developed plots and characters, maintaining consistency and creativity throughout the text. This makes it a powerful tool for creative writing and content creation.
8. Problem Solving
GPT-4 shows a marked improvement in solving complex mathematical and scientific problems. It can handle advanced calculus, simulate chemical reactions, and analyze scientific texts more effectively than GPT-3. This enhanced problem-solving capability extends GPT-4’s usefulness to fields that require high-level analytical skills, such as engineering and research.
9. Programming Abilities
In programming, GPT-4 significantly advances over GPT-3. It can generate and debug code more efficiently, making it a valuable asset for software developers. GPT-4 can not only write and fix code but also optimize and enhance its performance, wrapping it in graphical user interfaces if needed. This capability can drastically reduce development time and improve productivity in software projects.
10. Image Understanding
GPT-4 introduces the ability to analyze and comment on images and graphics, a feature not present in GPT-3. This allows GPT-4 to describe photos, interpret graphs, and generate image captions. This multimodal capability enhances GPT-4’s application scope, making it suitable for educational tools, content creation, and data analysis.
GPT 3 vs. GPT 4: How to Choose?
When choosing a GPT version, organizations should evaluate the following considerations.
Application Requirements
Consider the specific requirements of your application. If your use case involves simple text generation tasks or operates in environments with limited computational resources, GPT-3 may be sufficient. However, for more complex tasks that require nuanced understanding, multimodal capabilities, or real-time responsiveness, GPT-4 would be the better choice.
Budget and Resources
GPT-4’s advanced capabilities come with higher computational and financial costs. If your project has strict budget constraints or limited access to high-performance computing, GPT-3 might be a more practical option. For organizations with more substantial resources, investing in GPT-4 could provide a better return on investment through improved performance and broader application scope.
Customization Needs
Determine the level of customization your application demands. GPT-4’s more modular architecture allows for greater customization and fine-tuning, which can be crucial for specialized applications requiring tailored performance. If your application requires significant model customization to meet specific needs, GPT-4’s flexibility can be an advantage.
Ethical Considerations
Consider the importance of ethical AI practices in your application. GPT-4’s enhanced mechanisms for bias mitigation and safer outputs make it more suitable for applications where ethical considerations are a priority. If your use case involves sensitive contexts where ethical AI is a priority, GPT-4 can provide a more reliable and responsible solution.
Future-Proofing
Think about the future scalability and longevity of your solution. GPT-4 represents the latest advancements in AI language models, offering capabilities that are likely to remain state-of-the-art for a longer period. Choosing GPT-4 can future-proof your application, ensuring it stays relevant and competitive as technology continues to evolve.
Building LLM Applications with GPT-3, GPT-4, and Acorn’s GPTScript
Visit https://gptscript.ai to download GPTScript and start building today. As we expand on the capabilities with GPTScript, we are also expanding our list of tools. With these tools, you can create any application imaginable: check out tools.gptscript.ai to get started.