The download option for Chat GPT, the latest large language model developed by OpenAI, is now accessible. This model is capable of generating text outputs from both image and text inputs, making it a powerful multimodal tool.
The release of GPT-4 is a significant achievement in the field of artificial intelligence, particularly in natural language processing. In this article, we delve into the evolution of Generative Pre-trained Transformers (GPT) and examine the novel capabilities unlocked by GPT-4. Additionally, we provide a detailed analysis of its advanced features.
What exactly are Generational Pre-trained Transformers?
Generative Pre-trained Transformers” (GPT) is a type of deep learning model that produces text that closely resembles human writing.. Its applications include answering questions, summarizing text, translating languages, generating code, and creating various types of content, such as blogs, stories, and conversations. GPT models can be adapted to specific datasets for more accurate results, and their versatility allows for a range of applications. Using transformers can save resources, such as computing power and time, resulting in cost savings.
Prior to Chat GPT
The AI revolution in natural language processing was made possible by transformer models, starting with Google’s BERT in 2017. Prior to this, other deep learning models, such as recursive neural networks (RNNs) and long short-term memory neural networks (LSTMs), were used for text generation. However, they were limited to producing short phrases or single words and were unable to generate longer, realistic content.
BERT’s transformer approach was a significant breakthrough because it doesn’t rely on supervised learning, which eliminates the need for expensive annotated datasets during training. While Google used BERT to interpret natural language searches, it cannot generate text from a given prompt.
GPT-1
OpenAI published a paper in 2018 titled “Improving Language Understanding with Generative Pre-Training,” which detailed their use of the GPT-1 language model for natural language processing. However, this model was not publicly available as it was only a proof-of-concept at the time.
GPT-2
The year after their GPT-1 language model, OpenAI released another paper on their most recent model at the time, GPT-2, entitled “Language Models are Unsupervised Multitask Learners.” Unlike its predecessor, GPT-2 was made available to the machine learning community, and some individuals began using it for tasks such as generating text. Despite its success in producing a few sentences, GPT-2 would often break down. In 2019, this was considered cutting-edge technology.
GPT-3
In 2020, OpenAI published yet another paper on their GPT-3 model, titled “Language Models are Few-Shot Learners.” Thanks to its training on a larger text dataset and its 100-fold increase in parameters compared to GPT-2, the model exhibited superior performance. The GPT-3.5 series of updates, which introduced conversation-focused models like ChatGPT, further refined the model’s capabilities.
The world was amazed by the model’s ability to produce human-like pages of text, and within two months of its release, ChatGPT attracted 100 million users, making it the fastest-growing web app ever.
In a separate article, you can find out more about GPT-3, its uses, and how to use it..
Also read: ChatGPT at Highest Capacity: How To Fix It?
What Has Changed in GPT-4?
The development of GPT-4 focused on improving model “alignment,” which refers to its ability to adhere to user intent while also generating output that is less likely to be offensive or harmful and more truthful.
Improvements in performance
Compared to GPT-3.5 models, GPT-4 models exhibit higher accuracy in generating answers. On OpenAI’s internal factual performance benchmark, GPT-4 scored 40% better than GPT-3.5, indicating a decrease in the number of “hallucinations” or factual and reasoning errors committed by the model.
Moreover, GPT-4 improves “steerability,” or its ability to adjust to user requests. For example, it can be instructed to write in a different style, tone, or voice and explain a data science concept by starting prompts with phrases like “You are a garrulous data expert” or “You are a terse data expert.” Further details on creating effective prompts for GPT models can be found elsewhere.
Another improvement is the model’s adherence to guardrails, meaning it is better at not carrying out instructions that are illegal or harmful if requested.
Utilizing GPT-4’s Visual Inputs
GPT-4 has the capability to process both text and image inputs (currently available only as a research preview and not accessible to the general public). This feature allows users to specify any language or vision task involving both text and images.
Examples demonstrate ChatGPT’s ability to correctly interpret complex images such as charts, memes, and academic paper screenshots, highlighting the model’s potential for multimodal applications.
GPT-4 Execution Benchmarks
OpenAI conducted multiple tests on GPT-4, including simulating human-designed exams like the SAT, Uniform Bar Examination, and LSAT. The results showed that GPT-4 achieved human-level performance on various academic and professional benchmarks.
Moreover, GPT-4 outperformed existing large language models and most recent models on traditional machine learning benchmarks, including grade-school science questions, common sense reasoning, and multiple-choice questions in 57 subjects.
OpenAI also tested GPT-4’s ability to translate 14,000 multiple-choice questions across 26 languages using Azure Translate. The results revealed that GPT-4 performed better than GPT-3.5 and other large language models in 24 of the 26 languages tested.
Overall, OpenAI’s continuous efforts to create AI models with more advanced capabilities have yielded significant progress, as demonstrated by the impressive results of GPT-4.
How to Get Your Hands on GPT-4
OpenAI has made the text input feature of GPT-4 available through ChatGPT, but only for ChatGPT Plus users. As for the capability to input images, it is not yet accessible to the general public.
Furthermore, OpenAI has open-sourced OpenAI Evals, a framework for automated evaluation of AI model performance, enabling anyone to report model flaws and contribute to future improvements.
Take it to a Higher Level
Here are some additional resources to read about GPT-4, ChatGPT, and AI:
- The Introduction to ChatGPT course offers a comprehensive guide on how to use ChatGPT effectively.
- The Natural Language Generation in Python course provides insights on how to create your own deep-learning text generation models.
- You can download a handy cheat sheet of ChatGPT data science prompts for future reference.
- Listen to this podcast episode to learn how GPT-3 and ChatGPT are transforming workflows and how they can benefit your company.
Also read: Understanding ChatGPT, And Why It’s Even Bigger Than You Think
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