The Differences Between GPT-3.5 and GPT-4
In March 2023, OpenAI released GPT-4, the successor to GPT-3.5, on Pi Day (14.03.2023). According to OpenAI, GPT-4 represents a significant advancement in AI technology, with improved safety and greater utility compared to its predecessors. In this blog, we will explore the main differences between GPT-3.5 and GPT-4.
To begin, let’s define what GPT is. GPT stands for “Generative Pre-trained Transformer” and is an AI language model created by OpenAI. Its purpose is to generate text that mimics human language by predicting the next word in a sentence based on the context of previous words.
GPT-3.5 is a variant of the GPT-3 language model and is an enhanced version of it, rather than a completely new model. It builds upon the success of GPT-3 by introducing new capabilities and features. GPT-4, on the other hand, is the next major iteration of the GPT series. It has more advanced capabilities than its predecessors and is likely to be a major step forward in the development of AI language models.
It is designed to offer improved safety and efficiency when generating responses. This state-of-the-art model is multimodal and can accept both text and image inputs while generating text outputs. It has demonstrated an ability to perform at a level comparable to human beings on various professional and academic tests. GPT-4 is recognized as a leading technology in this field, representing the forefront of cutting-edge development.
Now, let’s describe the differences of two models deeper.
Model Size and Capabilities
Compared to GPT-3.5, GPT-4 is significantly more advanced in natural language understanding and processing. This improvement leads to more precise, consistent responses that can even recognize and respond to the emotions expressed in text. Additionally, GPT-4 can handle long-form content creation, extended conversations, and document search and analysis. GPT-4’s training on a larger dataset and its use of a different training method have also significantly improved its performance, making it better at reasoning and synthesizing information from multiple sources. GPT-4 is even better at understanding and producing various dialects and responding to the text’s emotions such as sadness or frustration. OpenAI has made efforts to ensure that GPT-4 is safer and more factual than its predecessor, with 82% fewer disallowed content responses and 40% more factual responses in internal evaluations.
Coherence and Creativity
Another difference between the two models is in their ability to generate creative content. While GPT-3.5 is capable of producing imaginative output, GPT-4 takes it a step further by generating stories, poems, or essays that are not only creative but also exhibit improved coherence and creativity. GPT-4 has demonstrated the ability to craft a short story that features well-developed characters and a compelling plot, whereas GPT-3.5 may experience difficulty in maintaining consistency and coherence throughout the narrative. According to OpenAI, “GPT-4 is more creative and collaborative than ever before. It can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.”
Solving Complex Problems
GPT-4 has made significant advancements in its ability to comprehend and process complex scientific and mathematical concepts. As part of its mathematical abilities, it can solve complicated equations and perform a range of operations, including calculus, algebra, and geometry. GPT-4 is also capable of handling a variety of scientific fields, such as physics, chemistry, biology, and astronomy. With its advanced processing power and language modelling capabilities, GPT-4 can analyse intricate scientific texts and provide insights and explanations in these fields with ease. OpenAI asserts that GPT-4 can solve difficult problems with greater accuracy, thanks to its broader general knowledge and problem solving abilities.
GPT-4 has surpassed GPT-3.5 in terms of its ability to generate code snippets and debug existing code, making it a valuable tool for software developers. By leveraging the power of GPT-4, developers can achieve weeks’ worth of work in just a matter of hours, resulting in exceptional outcomes in record time. Software developers might want to explore the capabilities of GPT-4 by experimenting with the tool and asking their questions.
Image and Graphics Understanding
GPT-4’s impressive capabilities include the ability to accept images as inputs and generate captions, classifications, and analyses. This allows for valuable insights and information to be extracted from visual media, making it a powerful tool for content creation, education, and research. Additionally, GPT-4’s ability to comment on images and graphics can be particularly useful for businesses and marketers seeking to make data-driven decisions.
While GPT-4 offers impressive advancements in AI technology, there are some drawbacks to consider. One of the main challenges is the increased computational power required to run the model, which may limit accessibility for smaller organizations or individual developers who lack the necessary resources. Additionally, the high resource demand of GPT-4 can result in greater energy consumption during the training process, raising environmental concerns. Despite these challenges, GPT-4 remains at the forefront of AI development, with significant potential for revolutionizing a wide range of industries.
All in all, GPT-4 is bigger, smarter, multi-modal and more expensive than GPT-3.5. OpenAI accepts that it has many known limitations that they are working to address, such as social biases, hallucinations, and adversarial prompts. In order to overcome these limitations, enterprises may consider more defined and smaller models that are appropriate for their needs. Taking this corporate need into consideration, CBOT launched CBOT GPT which maintains comparable accuracy levels with other LLMs but offers more flexibility, customization and security. Please check out CBOT GPT to learn more about how companies get the most benefit from large language models.