Introduction
GPT-3(Generative Pre-trained Transformer 3) and GPT-4(Generative Pre-trained Transformer 4) are both natural language processing (NLP) tools developed by OpenAI that uses machine learning to generate text according to the text input that is provided by the user. When comparing GPT-3 vs GPT-4, it is obvious to some extent that GPT-4 is going to be better than its predecessor. GPT-3 was released in 2020 and quickly gained popularity due to its impressive language capabilities and versatility. ChatGPT-4, the latest version of the tool, was announced in late 2021 and was released for commercial use on 14 march 2023 and has since generated significant interest in the NLP community.
Choosing the right language model is crucial for achieving optimal results in NLP tasks. In the following article, we’ll explore the architectural distinctions between GPT-3 and GPT-4 and investigate how these discrepancies influence their individual performances and if GPT-4 is better than GPT-3.
Architecture
Both ChatGPT-3 and ChatGPT-4 use a transformer-based architecture, which is a type of neural network that is particularly effective in handling natural language tasks. Despite their apparent similarities, the architectures of these two systems harbor some notable differences that set them apart. Both of these systems are quite good at providing human feedback.
ChatGPT-3 Architecture
ChatGPT-3 uses a 175 billion parameter transformer-based architecture, which was the largest and most complex architecture of any NLP tool before ChatGPT-4. It consists of 96 attention layers, with each layer containing 16 heads. This architecture enables ChatGPT-3 to handle a wide range of language tasks, including language translation, question answering, and even creative writing.
ChatGPT-4 Architecture
On the other hand, OpenAI has been a little secretive about ChatGPT-4, and the details of its architecture have not yet been released. However, it is expected to have a larger parameter count than ChatGPT-3(approx. 100 trillion) and to incorporate new techniques and approaches to language processing.
The effects of architectural disparities on the efficacy of language models cannot be overstated. Take, for instance, the potential benefits of increased parameter counts, which can equip models to tackle more intricate language tasks, or the advantages of additional layers or heads, which can augment their capacity to capture the intricacies of language in all its manifold forms.
One more important note is that ChatGPT-3 is only a singular modal model which can only accept data in one format, in the shape of written text. On the other hand, ChatGPT-4 is based on a multi-modal model which can accept images and videos as well.
In the next sections, we will compare the performance, training data, language capabilities, use cases, and cost of ChatGPT-3 and ChatGPT-4 to further examine the differences between these two language models.
Performance
The performance of a language model is measured based on its accuracy and efficiency in handling NLP tasks. In this article, we’ll be conducting a head-to-head evaluation of ChatGPT-3 and ChatGPT-4 to gauge their respective performances in these critical areas.
Performance Benchmarks
ChatGPT-3 has set a benchmark in the field of NLP, achieving state-of-the-art results in several language tasks, including language translation, question answering, and even generating human-like text. It has been tested on several benchmarks, including the SuperGLUE benchmark, where it achieved a score of 88.4, the highest score ever achieved by an NLP model.
As of ChatGPT-4, its performance benchmarks are not yet available. However, given its larger parameter count and expected improvements in architecture, it is anticipated that ChatGPT-4 will outperform ChatGPT-3 as ChatGPT -3’s limited response of 3000 words compared to 25000 of ChatGPT -4’s.
Comparison of Accuracy and Efficiency
While both ChatGPT-3 and ChatGPT-4 are expected to have high accuracy in language tasks, their efficiency may differ due to differences in architecture and parameter count. ChatGPT-4 is expected to be more efficient than ChatGPT-3 due to improvements in architecture and efficiency techniques.
Through the testing done by OpenAI over the period of 6 months, it was found that ChatGPT-4 is 40% less likely to provide the user with made-up information and provide information that is more factual.
Examples of Applications
ChatGPT-4 is expected to outperform ChatGPT-3 in tasks that require higher complexity and precision, such as generating more natural language and understanding more complex sentence structures. This can be particularly useful in applications such as virtual assistants, chatbots, and automated customer service.
Training Data
The quality and quantity of training data can significantly impact the performance and generalization of a language model. Here, we will compare the training data used in ChatGPT-3 and ChatGPT-4.
Size and Scope of Training Data
ChatGPT-3 was trained on a massive dataset of over 45 TB of text data, consisting of various sources such as books, websites, and academic papers. The scope of the data includes a wide range of topics and languages, making it a highly versatile tool.
The training data used for ChatGPT-4 is not yet known, but it is rumored to be larger and more diverse than the dataset used for ChatGPT-3.
Differences in Quality and Quantity
The quantity and quality of training data used can impact the performance and generalization of the language model. ChatGPT-4 is rumored to use a higher quantity of training data, which can lead to better performance and generalization. Additionally, the quality of the training data is also important, as it can affect the accuracy and diversity of the language model’s output.
Overall, the training data used in ChatGPT-4 is rumored to be larger and more diverse, leading to improved performance and generalization compared to ChatGPT-3.
Language Capabilities
ChatGPT-3 and ChatGPT-4 both have impressive language capabilities, but there are some differences worth noting.
First, ChatGPT-4 has improved multilingual support compared to ChatGPT-3, with better performance in all languages, such as Arabic, Chinese, and Hindi. This makes ChatGPT-4 a more versatile option for businesses that operate in multiple countries.
Second, both models are capable of understanding and generating natural language, but ChatGPT-4 has shown some advancements in generating more coherent responses. This means that ChatGPT-4 may be a better option for tasks that require generating high-quality responses, such as customer service chatbots or language translation.
Use Cases
ChatGPT-3 and ChatGPT-4 have numerous use cases across a range of industries. Some examples of real-world applications include:
Customer service chatbots:
Both models can be used to create chatbots that can interact with customers and provide support. ChatGPT -4’s improved language generation capabilities could make it a better option for creating chatbots that can provide more natural and coherent responses.
Content creation:
Both ChatGPT-3 and ChatGPT-4 can be used to generate content, such as articles or product descriptions. ChatGPT -4’s improved language generation capabilities could make it a better option for creating higher quality and more engaging content.
Language translation:
Both GPT models can be used to translate text from one language to another. ChatGPT -4’s improved multilingual support could make it a better option for businesses that operate in multiple countries.
Text Summarization:
Both AI models can be used to summarize large quantities of text, which can save a lot of time of the user and help the user to extract the key points in minutes.
Speech recognition and transcription:
Both models can be used for speech recognition and transcription, which is useful for applications such as automated transcription of meetings or interviews.
When it comes to choosing the right model for your NLP needs, it’s important to consider the specific requirements of your application. If you require improved language generation capabilities or multilingual support, ChatGPT-4 may be the better option. However, if these requirements are not critical, ChatGPT-3 may be a more cost-effective choice.
Cost and Availability
ChatGPT-3 and ChatGPT-4 are both proprietary language models developed by OpenAI, and as such, access to these models is limited and subject to licensing agreements.
The cost of using these models varies depending on some factors, such as the size and complexity of the model and the intended use case. OpenAI offers several pricing tiers for each model, ranging from individual user access to enterprise-level licenses.
In terms of availability, ChatGPT-3 and ChatGPT-3.5 turbo is currently more widely available than ChatGPT-4, which is only accessible by a subscription plan for 20$ a month. However, OpenAI has made some of the underlying code for ChatGPT-4 available on GitHub, allowing researchers and developers to experiment with the technology and contribute to its development.
Future Developments
OpenAI has ambitious plans for the development of ChatGPT and natural language processing technology in general. The company has revealed that it aims to create a model that can perform any language task with human-like skill, and it is continually working on improving the accuracy, efficiency, and generalization capabilities of its models.
In terms of ChatGPT specifically, OpenAI has announced plans to release increasingly large and complex versions of the model in the coming years, with the ultimate goal of creating a model that can understand and generate natural language on par with a human. The company is also investing in research into new techniques and technologies that can improve the performance and capabilities of natural language processing models.
To stay up-to-date on the latest and upcoming developments in ChatGPT technology, interested parties can follow OpenAI’s research publications, attend conferences and events focused on natural language processing, and keep an eye on industry news and trends.
Conclusion
In conclusion, ChatGPT-4 and ChatGPT-3 are both powerful natural language processing tools that have their own unique strengths and weaknesses. Both of these GPT models respond to requests with great capabilities answering questions with detailed explanations; however, compared to GPT-3, GPT-4 provides significantly improved results, which are far more human-like and factual. ChatGPT-4 offers improved architecture, performance, and language capabilities over its predecessor. However, choosing the right language model for your NLP needs depends on several factors, including the specific use case, available resources, and desired level of performance.
Want to know more about how ChatGPT-4 is changing the world?
FAQs
- Is ChatGPT-4 more accurate than ChatGPT-3?
Yes, ChatGPT-4 is generally 40% more accurate than ChatGPT-3 due to its improved architecture and larger training data size.
- Which model is better for multilingual support?
ChatGPT-4 has better multilingual support than ChatGPT-3, thanks to its larger training data size and improved architecture.
- What industries can benefit from ChatGPT-3 and ChatGPT-4?
Both ChatGPT-3 and ChatGPT-4 have a wide variety of applications across different industries, including healthcare, finance, e-commerce, and customer service.
- How much does ChatGPT-4 cost compared to ChatGPT-3?
The cost of ChatGPT-4 varies depending on the intended use case and licensing model. Generally, ChatGPT-4 is available with a ChatGPT Plus subscription for 20$ a month. In contrast, ChatGPT-3.5 turbo is available for free.
- When will ChatGPT-5 be released?
OpenAI has not yet announced a release date for ChatGPT-5, but it is expected to continue making improvements in the performance and capabilities of its language models.
