GPT-4 vs GPT-3.5: How Do They Differ?

The main difference between GPT-4 and GPT-3.5 is in short term memory. While GPT-3.5 has a short memory of around 8000 words, the short term memory of GPT-4 is around 64,000 words. GPT-4 allows the use of plugins using which you can pull up-to-date data whereas GPT-3.5 has a limited database till September 2021.

In this article, we will be discussing the key difference between GPT-4 and GPT 3.5. Let us get started.

Table of Contents

• GPT-4 vs GPT 3.5
• What is GPT 4?
• What is GPT 3.5?
• GPT 3.5 vs GPT 4: Which is better?

GPT-4 vs GPT 3.5: Key Differences

GPT-4 is an updated version of GPT-3.5 with more parameters and enhanced understanding capabilities.

What is GPT 4?

Let us now discuss GPT 4, which is the latest version of Chat GPT app. GPT 4 is an advanced artificial intelligence model developed by Open AI. It is the successor of GPT-3 on the similar architecture but with some improvements. It is based on the transformer architecture that uses deep learning techniques for processing and generating human-like text. GPT-4 is trained on a huge amount of textual data fetched from the internet. This makes it capable of understanding the context, generating coherent responses and performing tasks such as summarization and translation.

Capabilities

Let us now discuss the capabilities of GPT-4:

• Understands complex sentences: GPT 4 is capable of understanding complex sentence structures, colloquialisms and idiomatic expressions. This ChatGPT model is trained on larger and more diverse data sets due to which it has a broader understanding of topics.
• Generative capabilities: ChatGPT-4 is able to generate human-like text in the form of conversations, stories, articles and even poetry.
• Reinforcement Learning Integration: GPT-4 has added enhanced reinforcement learning due to which it offers more effective learning based on user interaction and preferences.
• Enhanced word processing: GPT-4 can process and handle upto 25,000 words of text provided as input by the user.
• Plugins: GPT-4 allows the use of add on plugins such as “Metaphor”, “ScholasticAI” among other useful plugins. With the help of these plugins, GPT-4 can provide up-to-date data. Here is an example of how it works:

• Code Interpreter: This is a great feature of GPT-4 that leverages the features of large language models (LLMs) along with programming. You can run Python code and upload files. Using code interpreter, one can perform data analysis and visualization very efficiently.

• Multi-modal understanding: It can comprehend and process variations of data. GPT 4 can generate image and text prompts due to its problem-solving abilities. In the image shown below, GPT 4 interprets an image provided by the user.

• Allows Image Generation: With the inclusion of DALL-E 3, ChatGPT 4.0 allows image creation with the help of prompts. This is not available within ChatGPT 3.5.

What is GPT 3.5?

GPT 3.5 is an intermediate version of the GPT 3 language model that was released in May of 2021. This updated version of GPT 3 comes with improvements and several other new features. Here are the features of this version of GPT.

• Efficient: In comparison with its predecessors, GPT 3.5 has a smaller model size yet a faster inference time. This makes this version of Chat GPT more efficient. 
• Versatile: With the ability to perform more logical and arithmetic operations, GPT 3.5 is able to perform more tasks in comparison to previous versions of the Chat GPT app.
• Accurate: In GPT-3.5, there are additional pre-training steps which make this model more capable of understanding language in context. This allows GPT to provide more accurate responses to input prompts. Due to this accuracy, it is widely used in NLP (natural language processing) tasks.
• Few-Shot Learning: It can learn using a small amount of data due to its few-shot learning capability. The credit goes to the use of unsupervised pre-training. Here, the GPT model is trained on a large amount of text data without any specific task. It allows the model to learn underlying patterns as well as the structure of natural language.  
• Multilingual: It can generate text in multiple languages as the model is trained on a large amount of text in multiple languages. This allows Chat GPT to learn patterns and structures of different languages. When a text of a specific language is provided to GPT, it generates coherent and relevant responses. This makes it more versatile in handling multilingual applications.

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Capabilities of GPT

This version of Chat GPT was released This version of Chat GPT was released to improve language processing and anatural language generation capabilities in Artificial Intelligence models.

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Let us now discuss the capabilities of this version:

1. Chatbots and virtual assistants: GPT 3.5 can be used for creating chatbots and virtual assistants. These can understand and generate natural language responses to the queries of users. Through its few-shot learning, the chatbots and virtual assistants can quickly adapt to a set of new tasks and situations. With this version of the Chat GPT app, user experience is also improved as it provides more timely and accurate responses.
2. Sentiment analysis: This GPT version is capable of determining the sentiment behind a text by analyzing it. With the help of sentiment analysis, it can detect the opinion or even the attitude behind a text. GPT-3.5 can categorize text as neutral, positive and negative. This is a useful capability for customer feedback analysis, business decision making and marketing research.
3. Personalized Content: The GPT-3.5 version has the capability to generate human-like text in a variety of styles. Along with that, it has the few-shot learning capability, due to which it can quickly adapt and offer personalized content. One such example is providing personalized product recommendations based on the browser history of a user. Companies like eBay, Walmart and Amazon have leveraged it to create a personalized shopping experience for their customers.

1. Language translation: This version of ChatGPT also helps in machine translation and cross-lingual information retrieval. This retrieval can be carried out by inputting a search query in a language and then retrieving results in another language. By generating texts in multiple languages, it overcomes barriers of communication in languages and thus improves the accessibility to information. The below-given image is an example of cross-lingual information retrieval. Here, question/input is in English and the answer/output is in Spanish.

GPT-4 vs GPT-3.5: Which is better?

We have discussed both the versions of Chat GPT in detail. In terms of improvement, GPT-4 is a step ahead of GPT-3.5. Let us take a look at some examples:

1. Image Inputs: GPT-3.5 could only take and interpret textual inputs. GPT-4, on the other hand, can take both image and text-based inputs.

2. Accurate responses: Unlike the previous version, GPT-4 has improved contextual understanding due to which it gives more correct responses. Let us take a look at the responses to the same questions by both versions to understand this.

Response of GPT-3.5:

Response of GPT-4:

3. Filters out sensitive content: The mitigations in GPT-4 have improved its safety properties due to which its tendency to respond to disallowed content has decreased by 82% compared to GPT-3.5.

4. Outperforms Exams: GPT-4 has been able to outperform GPT-3.5 in many exams. Infact, it has been able to outscore humans on some of the toughest exams in the world.

Overall, it can be inferred that as of now, GPT-4 is better than its previous versions. It is more accurate, more sensitive in filtering out disallowed content and has improved contextual understanding. This makes it a much better update. However, it is not completely devoid of faults. It still makes mistakes and sometimes even gives out hate speech like its previous versions. One such noticeable prompt was DAN.

This does not overshadow the improved and enhanced capabilities of GPT-4, such as improved reinforcement learning. It outperforms most large language models on the collection of NLP tasks. On the other hand, its increased capabilities bring on new risks. Due to this, better methods need to be tested and adopted to enhance safety.