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Introduction
Since its release in 2020, GPT-3 (Generative Pre-trained Transformer 3) has made significant strides in natural language processing, setting a new standard for AI language models. However, as technology advances at an unprecedented rate, the next generation of AI models is already on the horizon, with GPT-4 (Generative Pre-trained Transformer 4) poised to take the torch from its predecessor. While GPT-3 has been lauded for its remarkable abilities, GPT-4 is expected to push the boundaries even further. This article at OpenGenus aims to inculcate a basic technical understanding of the differences between GPT-3 and GPT-4.
| Comparison Factor | GPT-3 | GPT-4 |
|---|---|---|
| Release | Released on 11 June 2020 | Released on 14 March 2023 |
| Architecture | Transformers with up to 96 attention layers | Transformers with even deeper architectures than GPT-3 |
| Parameters | 175 billion parameters | Estimated to have a trillion parameters |
| Training Data | Trained on a diverse range of internet text, reported to be 45TB in size. | Expected to be trained on even larger and more diverse datasets, around 1 PB in size. |
| Inputs | Accepts text input in natural language from the user. | It is a large multimodal model; it accepts both text and image inputs from the user. |
| Applications | Used for natural language processing, language translation, and text generation. | Expected to have even wider range of applications due to its multimodal nature, including various forms of image analysis. The scope of use is much higher than GPT-3. |
| Cost | Expensive to train and run due to large number of parameters. | Expected to be even more expensive to train and run due to larger number of parameters. |
Differences in Architecture
GPT-4 is expected to be the biggest language model to date, with up to a trillion parameters, much exceeding GPT-3's 175 billion. This tremendous increase in scale will most likely allow the model to acquire even more intricate and subtle natural language patterns, resulting in more accurate and human-like replies.
Another significant distinction between GPT-4 and GPT-3 is the addition of new modules and architectures. The "Attention Condenser" module in GPT-4 is supposed to assist the model focus on the most relevant sections of the input data, decreasing the computing resources required to analyse the input.
Furthermore, GPT-4 will use a multi-modal approach, which implies that the model will be able to process many forms of input, enabling the model to understand and generate responses that incorporate multiple modalities, making it more human-like in its interactions.
GPT-4 will also include novel training approaches such as unsupervised pre-training and fine-tuning. This novel approach is expected to allow the model to learn from greater quantities of data and to be more adaptive to varied tasks, resulting in improved performance across a variety of natural language processing activities.
Overcoming the limitations of GPT-3.
Since GPT-3 is trained on a one-way communication format, one of it's key drawbacks is its inability to perform actual two-way conversations. GPT-4 can overcome this issue by introducing a new module known as "Dialoguer" , which allows the model to engage in more involved and dynamic dialogues. The Dialoguer module is designed to enable GPT-4 to ask questions, seek explanation, and react in a more conversational manner, leading in more realistic and human-like interactions.
A further drawback of using GPT-3 is its dependency on huge quantities of computing resources, which might make access to the model difficult for smaller organisations or individuals. Although GPT-4 will most likely need additional processing resources because to its bigger scale, it will also include new hardware and software optimisations to enhance efficiency and save costs.
Furthermore, GPT-3 has been criticised for spreading prejudice and stereotypes that existed in the data it was trained on. GPT-4 can solve this issue by adopting new strategies to reduce bias in training data and ensuring that the model provides more inclusive and representative replies.
To conclude, GPT-4 is projected to address some of GPT-3's limits and drawbacks by introducing new modules and architectures, eliminating bias concerns, and enabling more participatory and dynamic conversations. While GPT-4 could require more processing resources than GPT-3, it will also include new qualitative and quantitative approaches to enhance efficiency and lower costs, making it more accessible to a broader variety of users.
