Evolution of AI Language Models: A Comparative Analysis of GPT-3.5 and GPT-4

Hello there! This week, let's understand the differences between GPT-3.5 and GPT-4 in various aspects. Artificial intelligence has witnessed a remarkable evolution in recent years, with language models playing a central role in driving this progress. OpenAI's GPT series stands as a testament to the continuous innovation in this domain. In this blog, we delve into the advancements introduced by GPT-3.5 and GPT-4, shedding light on their distinctive features and implications and a detailed comparison of these two models.

GPT-3.5

GPT-3.5 serves as a transitional phase between its predecessor, GPT-3, and the groundbreaking GPT-4. While specific details regarding its architecture remain undisclosed, OpenAI focused on optimizing speed and reducing operational costs. Notably, GPT-3.5 introduced specialized variants such as GPT-3.5 Turbo, tailored for chat applications, showcasing its versatility across various domains.

Key Capabilities of GPT-3.5

• Natural language understanding and generation for diverse tasks.
• Language translation with reasonable fluency.
• Chatbot functionality with GPT-3.5 Turbo.
• Summarization of lengthy text.
• Code generation and problem-solving capabilities.

GPT-4

GPT-4 emerges as a leap forward in AI language models, offering unprecedented capabilities and reliability. Unlike its predecessors, GPT-4 embraces multimodality, accepting both text and image inputs to generate rich and contextually relevant responses. Furthermore, it boasts larger context windows, broader general knowledge, and enhanced safety features, marking a significant stride towards human-level performance.

The transition from ChatGPT 3.5 to ChatGPT 4 signifies a paradigm shift in AI capabilities, with the latter being hailed as an early version of artificial general intelligence (AGI). Notable improvements include multimodal inputs, heightened safety, and expanded memory capacity, elevating ChatGPT 4's performance across various domains.

Key Capabilities of GPT-4

• Multimodal inputs for text and image processing.
• Extended context windows for handling extensive text inputs.
• Enhanced reliability, creativity, and alignment with user intentions.
• Improved safety measures to mitigate the risks associated with misinformation and bias.

A Detailed Comparison of GPT-4 vs. GPT-3 and GPT-3.5

Exploring the disparities between GPT-4 and its predecessors, Greg Brockman, OpenAI's co-founder, succinctly characterizes GPT-4's evolution with a single word: "Different." This distinction becomes more apparent upon closer inspection, particularly in terms of capabilities and performance.

Accuracy

GPT-3.5, trained only up to September 2021, lacks accuracy in handling recent queries. Conversely, GPT-4 is equipped to browse the internet and is trained on data up to April 2023 or December 2023, depending on the version.

Enhanced Capabilities

GPT-4 exhibits superior performance across various benchmarks, excelling in complex tasks like calculus and law. In simulated exams such as the AP Calculus BC exam, GPT-4 significantly outperforms its predecessors. While GPT-3 scored a mere 1 out of 5, GPT-4 achieved a score of 4. Similarly, in a simulated bar exam, GPT-4 attained a top 10% score, while GPT-3.5 – the most advanced iteration of the GPT-3 series – ranked in the bottom 10%.

Furthermore, GPT-4 showcases impressive multilingual proficiency, with an accuracy rate surpassing 85% across 25 languages, highlighting its versatility.

Expanded Context Length

Unlike the earlier models, GPT-4 boasts an extended context length, enabling the processing of much longer text in a single request. With variants accommodating up to 32,768 tokens, GPT-4 opens new avenues for analyzing and summarizing extensive documents without losing context.

Multimodal Input Processing

GPT-4 breaks free from the confines of text-only input by embracing a multimodal approach that incorporates images. This innovative capability allows GPT-4 to generate text outputs based on combined text and image inputs, expanding its utility in academic research, content creation, and beyond.

Contextual Control

Another significant advancement in GPT-4 is its ability to adhere to predefined contextual parameters, ensuring consistency and alignment with user specifications. Through system messages, users can dictate the model's tone, style, and behavior, enhancing its adaptability across various scenarios.

While GPT-3.5-Turbo offers a glimpse of this capability, GPT-4 surpasses it in effectiveness. In our projects, we observed GPT-4's superior adherence to system message behavior compared to GPT-3.5.

Example

1. GPT-3.5 Turbo

System Message: "You are a cooking assistant who will provide me only cooking-related information and help me prepare food by giving information on how to cook."

Query: "Do you know when was the first Republic Day of India celebrated?"

Response: "Yes, the first Republic Day of India was celebrated on January 26, 1950. It was on this day that the Constitution of India came into effect, replacing the Government of India Act (1935) as the governing document of India."

This response deviates from the specified behavior in the system message.

2. GPT-4

For the same system message and query specified above, GPT-4 provided the following response:

Response: "As a cooking assistant, my primary focus is on helping you with cooking-related questions and guidance. However, to answer your question briefly, the first Republic Day of India was celebrated on January 26, 1950. If you're planning to prepare a meal for Republic Day celebrations or any other event, feel free to ask for recipes, cooking tips, or any other culinary advice you might need!"

Despite providing information unrelated to cooking, GPT-4 skillfully connects it with cooking and offers an engaging response.

Cost Considerations

Although the benefits of GPT-4 are undeniable, they come at a premium. Compared to its predecessors, GPT-4 commands a higher price, reflecting its enhanced capabilities and performance. Users must carefully consider the benefits against the associated costs, with pricing structures varying based on context length and token usage.

Boasting a context length of 128k, updated knowledge, and an extensive range of capabilities, GPT-4 Turbo surpasses GPT-4 in power and versatility, all while being available at a more affordable price point.

Errors and limitations

While it's challenging to deem GPT-4 disappointing given its impressive creativity and capabilities, it's crucial to acknowledge its limitations. As stated in the product research documentation, these limitations haven't undergone significant changes compared to previous model versions.

Similar to its predecessors, GPT-4 lacks knowledge of events occurring after April 2023. Furthermore, despite its advancements, GPT-4, like any AI model, isn't infallible. Even with its reduced tendency for errors compared to earlier iterations (reporting a 40% decrease in hallucinations relative to GPT-3.5 in internal evaluations), it still occasionally produces inaccurate information and reasoning errors. While it's more inclined to decline providing harmful advice, produce buggy code, or generate misleading content, it remains unsuitable for applications where error costs are high.

That's about it for now! We hope this was an informative piece and you enjoyed reading it as much as we did writing! We'll see you around next week.