Comparison of GPT-4 and GPT-3.5

GPT-4 and GPT-3.5 are two large language models created by OpenAI, a research organization dedicated to creating artificial intelligence that can benefit humanity. Both models are trained on vast amounts of online data to generate complex responses to user prompts, but they have some key differences in their capabilities, architectures and applications.

What is GPT-4?

GPT-4 (Generative Pre-trained Transformer 4) is a multimodal large language model that can analyze not only text, but images as well . The exciting addition of “computer vision” allows the AI’s users to input photos and drawings, and get relevant text outputs based on them. For example, you can ask GPT-4 to describe what is happening in an image, or to generate a caption for it.

GPT-4 was released on March 14, 2023 , and will be available via API and for ChatGPT Plus users. Microsoft confirmed that versions of Bing using GPT had in fact been using GPT-4 before its official release.

GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that exhibits human-level performance on various professional and academic benchmarks. However, it is still worse than humans in many real-world scenarios, such as understanding context, common sense reasoning and ethical implications.

What is GPT-3.5?

GPT-3.5 (Generative Pre-trained Transformer 3.5) is an intermediate version between GPT-3 and GPT-4 that was trained using Reinforcement Learning from Human Feedback (RLHF) . This means that instead of relying solely on online data for training, GPT-3.5 also learned from human feedback on its outputs. This improved its ability to generate engaging and coherent dialogues with humans.

GPT-3.5 was finished training in early 2022, and was used as the basis for ChatGPT , a fine-tuned version of GPT-3.5 that’s essentially a general-purpose chatbot. ChatGPT was launched on November 30th, 2022, and allows users to have natural conversations with an AI agent about various topics.

GPT-3.5 models can understand and generate natural language or code. Our most capable model in the GPT-3 series is gpt-davinci-codex which has been optimized for code generation but works well for traditional completions tasks as well.

How do they compare?

Both GPT-4 and GPT-3.5 are impressive achievements in natural language processing and artificial intelligence research. They have some similarities and differences in their capabilities, architectures and applications.

Capabilities

Both models can generate complex responses to user prompts based on their training data. However, they have different strengths and weaknesses depending on the type of input and output required.

Text input

For text inputs such as questions or statements, both models can produce relevant answers or continuations based on their knowledge base. However,

1. GTP — 4 has an advantage over GTP — 3 . 5 when it comes to generating long-form texts such as essays , summaries or stories , as it can maintain coherence , structure , style , tone , grammar , spelling , punctuation better than its predecessor .
2. GTP — 4 has an advantage over GTP — 3 . 5 when it comes to generating factual texts such as reports , articles or reviews , as it can access more up-to-date information from its larger data set

GPT-4 has three main features that distinguish it from its predecessors:

• It can see: GPT-4 can process images as well as text, which enables it to perform tasks such as image captioning, visual question answering, image generation from text descriptions, etc .
• It can learn: GPT-4 uses reinforcement learning with human feedback to improve its alignment with human instructions and preferences . This allows it to adapt to different domains and tasks more easily than previous models.
• It can scale: GPT-4 has a massive parameter count of 500 billion, which is almost three times larger than GPT-3.5’s largest model . This gives it more expressive power and generalization ability across various domains.

Some of the disadvantages of GPT-4 compared to GPT-3.5 are:

1. It requires more computational resources and energy consumption to train and run, which makes it less accessible and sustainable for many users and applications.
2. It may still generate harmful or biased outputs, despite its reinforcement learning mechanism, which makes it less trustworthy and ethical for some scenarios.

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Originally published at http://thetechsavvysociety.wordpress.com on March 17, 2023.