The field of natural language processing has witnessed remarkable advancements over the years, with the development of cutting-edge language models such as GPT-3 and the recent release of GPT-4. These models have revolutionized the way we interact with language and have opened up new possibilities for applications in various domains, including chatbots, virtual assistants, and automated content creation.
What is GPT?
GPT is a natural language processing (NLP) model developed by OpenAI that utilizes the transformer model. Transformer is a type of Deep Learning model, best known for its ability to process sequential data, such as text, by attending to different parts of the input sequence and using this information to generate context-aware representations of the text.
What makes transformers special is that they can understand the meaning of the text, instead of just recognizing patterns in the words. They can do this by “attending” to different parts of the text and figuring out which parts are most important to understanding the meaning of the whole.
For example, imagine you’re reading a book and come across the sentence “The cat sat on the mat.” A transformer would be able to understand that this sentence is about a cat and a mat and that the cat is sitting on the mat. It would also be able to use this understanding to generate new sentences that are related to the original one.
GPT is pre-trained on a large dataset, which consists of:
As, it has been trained on vast amounts of text from the internet and other sources, such as books and articles, it uses this knowledge to generate new text that sounds like it was written by a human. For example, you could give GPT a prompt like “Write a short story about a cat and a mouse who become friends”, and it would generate a unique story based on what it has learned from the text it has been trained on. This technology has many potential applications, such as automated content creation, chatbots, and virtual assistants.
Overall, GPT is an impressive tool that has the potential to transform how we interact with computers and make our interactions with them more natural and intuitive.
GPT Architecture Overview
The GPT architecture consists of a series of layers, including
- an input/embedding layer,
- that converts the input(sequence of tokens) into embeddings
- multiple transformer/attention blocks
- to process the input and generate a contextual representation of the input.
- an output/classifier layer.
- which generates a probability distribution over the possible next tokens in the sequence.
- The model can then use this probability distribution to generate text by sampling from it.
The model was then fine-tuned using Reinforcement Learning from Human Feedback to generate more human-like output.
To get a more detailed explanation of ChatGPT architecture, you can click on the below image.
GPT Use-Cases
GPT is now being utilized in numerous real-world applications, making it challenging to compile an exhaustive list of its use cases within a single article. Some of the most prevalent uses of GPT include:
- Automated content creation: GPT can be used to generate text content for websites, blogs, and social media posts. By giving it a prompt, such as a topic or a keyword, it can generate high-quality, human-like content that can be used to fill out a website or social media page.
- Chatbots and virtual assistants: GPT can power chatbots and virtual assistants that can communicate with users in a natural language. By training the model on conversation data, it can understand the intent of the user’s message and generate a response that feels like it was written by a human.
- Language translation: GPT can be used to translate text between different languages. By training the model on parallel texts in two different languages, it can generate translations that are often more accurate and natural-sounding than traditional machine translation methods.
- Content summarization: GPT can be used to summarize long articles or documents into shorter, more manageable summaries. By training the model on large amounts of text data, it can identify the most important parts of an article and generate a summary that captures the key points.
- Storytelling and creative writing: GPT can be used to generate original stories or creative writing based on a prompt or theme. By training the model on a large corpus of literature and creative writing, it can generate stories that are unique, engaging, and sound like they were written by a human.
- Question answering: GPT can answer questions based on a given context, making it useful in search engines and information retrieval systems.
- Personalization: GPT can be used to provide personalized recommendations and content based on user behavior and preferences.
Advancements of GPT-4 over GPT-3
ChatGPT, the latest advancement in the field of Artificial Intelligence, has so far employed the GPT-3/3.5 version. However, it has now introduced the option of GPT-4 access for its “Plus members.” Let’s take a look at the distinctions between these two models.
Improvements of GPT 4 over GPT 3
- GPT-4 can see and understand images: While GPT-3 and GPT-3.5 were restricted to processing only text data, GPT-4, is a multimodal model. This means that it can handle both text and images, allowing it to extract relevant information from images as well as text.
User: What is funny about this image? Describe it panel by panel.
GPT-4: The image shows a package for a "Lightning Cable" adapter with three panels.
Panel 1: A smartphone with a VGA connector (a large, blue, 15-pin connector typically used for computer monitors) plugged into its charging port.
Panel 2: The package for the "Lightning Cable" adapter with a picture of
a VGA connector on it.
Panel 3: A close-up of the VGA connector with a small Lightning connector (used for charging iPhones and other Apple devices) at the end.
The humor in this image comes from the absurdity of plugging a large,
outdated VGA connector into a small, modern smartphone charging port.
- Better performance in more complex and nuanced scenarios: To test its capabilities in such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In these evaluations, it performs quite well and often outscores the vast majority of human test takers. For example, on a simulated bar exam, GPT-4 achieves a score that falls in the top 10% of test takers. This contrasts with GPT-3.5, which scores in the bottom 10%. Note that, no explicit training was done for these exams.
- Gives more precise results based on the given input prompt: GPT-4 substantially improves over previous models in the ability to follow user intent. On a dataset of 5,214 prompts submitted to ChatGPT and the OpenAI API, the responses generated by GPT-4 were preferred over the responses generated by GPT-3.5 on 70.2% of prompts.
- More Multilingual: Since most Machine Learning models are trained on English language data, Language Learning Models (LLMs) can encounter difficulties when processing languages other than English. However, GPT-4 has been specifically designed to comprehend other languages more effectively than its predecessors. It is able to answer thousands of multiple-choice questions with high accuracy across 26 languages, from Italian to Ukrainian to Korean. What this means is that users will be able to use chatbots based on GPT-4 to produce outputs with greater clarity and higher accuracy in their native language.
- Harder to Manipulate: Although GPT-3 was an impressive model, it was susceptible to being manipulated by users, which resulted in the generation of false information.
GPT-4 has been designed to address the issue of susceptibility to manipulation by incorporating enhanced security features, including improved algorithms and techniques such as stronger pre-training and fine-tuning methods. These measures aim to reduce the likelihood of GPT-4 generating false information and enhance its ability to produce more reliable outputs.
- Predictable Scaling: A large focus of the GPT-4 project was building a deep learning stack that scales predictably. The primary reason is that for very large training runs like GPT-4, it is not feasible to do extensive model-specific tuning. To address this, OpenAI developed infrastructure and optimization methods that have very predictable behavior across multiple scales. These improvements allow to reliably predict some aspects of the performance of GPT-4 from smaller models trained using 1, 000× – 10, 000× less compute.
Limitations of GPT 4
Despite its capabilities, GPT-4 has similar limitations as the earlier GPT model.
- It generally lacks knowledge of events that have occurred after the vast majority of its pre-training data cut off in September 2021, and does not learn from its experience.
- GPT 4 can sometimes make simple reasoning errors that do not seem to comport with competence across so many domains or be overly gullible in accepting obviously false statements from a user.
- It can fail at hard problems the same way humans do, such as introducing security vulnerabilities into the code it produces.
- GPT-4 can also be confidently wrong in its predictions, not taking care to double-check work when it’s likely to make a mistake.
- As GPT-4 has greater capability than its predecessors, it poses new potential risks. However, the developers are proactively working to mitigate these risks, and the latest updates have been effective in minimizing them.
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