OpenAI’s GPT-4: How it is different from its predecessor GPT-3.5, With ChatGPT’s ongoing success and popularity, OpenAI has now created GPT-4, the highly anticipated successor to GPT-3.5. GPT-4 is a large multimodal model capable of accepting text and image inputs and generating text outputs.
With its unmatched language-generating capabilities, GPT-3.5 has raised the bar for natural language processing (NLP). OpenAI’s GPT-4 is expected to push the boundaries of NLP even further and enable the development of more advanced and sophisticated language-based applications.
OpenAI’s technical report states that GPT-4 has demonstrated human-level proficiency in academic and professional environments, including achieving scores in the top 10% of test takers on the bar exam. While GPT-4 continues to utilize the Transformer architecture, it surpasses its predecessors by exhibiting enhanced capabilities in comprehending the subtleties of language, such as its context, mood, and significance.
One of the most impressive feats of GPT-4 is its ability to understand and follow user intent. This may have significant implications in many sectors, including finance, healthcare, education, etc. Additionally, its advanced NLP capabilities could lead to the development of more efficient and accurate virtual assistants, such as chatbots.
In this article, we will focus on understanding the main differences between GPT-4 and GPT-3.5 in terms of performance and training. We will also take a look at GPT-4’s release and which industries are most likely to benefit from it.
GPT 3.5: What are its capabilities?
In 2022, OpenAI released ChatGPT based on the GPT-3.5 series. This is a series of models that have been trained on a blend of text and code, with the data or information being used dating to before September 2021.
GPT stands for generative pre-trained transformer and is a language model that uses neural networks to generate human-like text. The largest model in GPT-3.5 has 175 billion parameters (the training data used is referred to as the ‘parameters’) which give the model its high accuracy compared to its predecessors. ChatGPT is capable of language translation, writing various types of creative content, and answering user questions in an informative way.
The output from ChatGPT is of very high quality, often making it difficult to distinguish between human and machine-generated text. Among other things, ChatGPT has been used to generate news articles, write poems, and create chatbots that can hold conversations with humans. Since its launch, ChatGPT has surprised users with its powerful technology with a variety of applications in many fields.
A brief overview of GPT-4 and its capabilities
Similar to its predecessors, GPT-4 is a language model capable of generating human-like responses. The exact architecture of GPT-4 and the amount of training data used with the model have not been revealed by OpenAI. According to their report, GPT-4 can accept input in the form of images and text and provide responses accordingly.
The main aim behind their development of this version was to improve previous GPT models’ responses in complex scenarios and fine-tune responses based on human feedback. This is considered a significant improvement, allowing the model to align more closely with human intent.
The model was used in various professional, academic, and social scenarios to test its capabilities. They found that GPT-4 performed excellently — comparable to humans. In particular, the model scored in the top 10% of test takers for the Uniform Bar Examination and did well in other tests, such as the SAT. Scientists and developers from OpenAI believe that the excellent performance of the model is highly reliant on the pre-training process.
Undeniably, GPT-4’s most exciting feature is its ability to accept input in the form of images or visuals. The input visuals can be in the form of documents with texts and photos, diagrams, or screenshots. Additionally, the model has also shown the ability to identify humor in visual inputs. This means that it can not only generate funny text but it can also recognize and explain jokes in images.
Also Read: Top 5 Ways GPT-4 Can Increase Workers Productivity
Performance comparison between GPT-3.5 and GPT-4
GPT-4 has shown improved performances in many different situations compared to GPT-3.5. According to early reports by users and comments by OpenAI’s co-founder, GPT-4 is better than GPT-3.5 at producing creative writing, and it is capable of generating poems and other creative text. Additionally, GPT-4 can correct itself when it makes a mistake and produce a perfect response, which is lacking in GPT-3.5.
Another area where GPT-4 outperforms GPT-3.5 and other state-of-the-art models is in exam-taking. GPT-4 is acing exams and tests, even the challenging ones like the bar exam. This is an exciting development and may be used as a teaching (or cheating) aid in schools.
GPT-4 is also showing promise in the area of massive multi-task language understanding (MMLU). This is a benchmark that measures the knowledge acquired by a model during pretraining. GPT-4 has demonstrated excellent performance in a total of 27 languages, including English.
GPT-4’s improved factual accuracy is a significant development. This means that users can be more confident that the information they receive from GPT-4 is accurate and up-to-date. This is especially important in areas such as learning, technology, writing, history, math, science, recommendation, code, and business.
GPT-4’s improved factual accuracy is likely due to several factors, including its larger dataset, its more sophisticated training methods, and its ability to learn from feedback. However, this cannot be said with certainty since the training methods have not been disclosed. However, it is likely that with continued development, GPT-4’s factual accuracy will improve even further.
Potential applications of GPT-4
Due to its multimodal interface, GPT-4 has the potential to revolutionize many industries, including customer service, education, and entertainment. It can also improve existing technologies and research by improving chatbots and further advances in machine learning (ML) research.
Customer service
GPT-4 can be used to automate customer service tasks, such as answering questions, resolving issues, and providing support. This would allow human customer support representatives to concentrate on more complicated problems that would require more time and effort.
Education
The model can be used to create educational content, such as interactive lessons, practice exercises, and assessments. By allowing students to interact with the technology in real time, teachers can get real-time feedback on how well students are understanding the material.
Entertainment
It can also be used to create entertainment content, such as stories, poems, and music. For example, it can be used to generate realistic and nuanced dialogues for movies, TV shows, and video games. This can help to make these products more immersive and engaging for users, while also freeing up the creator’s time to focus on more technical aspects of their work.
Improving chatbots
GPT-4 can be used to improve existing chatbots by making them more human-like and engaging. Chatbots powered by GPT-4 can hold conversations that are more natural and nuanced, and they can provide more helpful and informative answers to questions.
Further advancements in machine learning research
Finally, GPT-4 can be used to further machine learning (ML) research. By studying how GPT-4 can generate responses in different forms, researchers can develop new and innovative ML algorithms that can improve on the fallacies of existing models.
These are just a few examples of the potential applications of GPT-4. As GPT-4 continues to develop, we will likely see even more innovative and creative uses for this technology.
Limitations of GPT-4
Similar to other language models, GPT-4 also has certain limitations. Some of these limitations include bias, accuracy, and safety. Let’s take a look at each of them below.
Bias: Since GPT-4 is trained on a large dataset of text and code, the model will inherit any existing biases in the dataset.
Accuracy: Just like its predecessors, GPT-4 is capable of making factual mistakes and providing inaccurate or misleading information.
Safety: GPT-4-generated material has the potential to be harmful and degrading. As a result, when using the model, it is critical to be conscious of this danger. It is good to be aware of these limitations when using GPT-4, because it can help users to take appropriate actions to mitigate the risks and use GPT-4 to its full potential.
