Vinija's Notes • Models • Generative Pre-trained Transformer-4 (GPT-4)

Overview

On Pi day (3/14/2023), OpenAI unveiled their latest GPT model, GPT-4, which boasts a multimodal interface that takes in images and texts and emits out a text response.
GPT-4 was trained on a large dataset of text from the internet and fine-tuned using RLHF.
The GPT-4 Technical Report gives us a glimpse of how GPT-4 works and it’s capabilities and that is what I have explained below.

Let’s do a quick refresher on GPT:
- During pre-training, the model is trained to predict the next word in a sequence of text given the previous words.
- Once the model is pre-trained, it can be fine-tuned on a specific task by adding a few task-specific layers on top of the pre-trained model and training it on a smaller dataset that is specific to the task.
While the paper is lacking on technical details about GPT-4, we can still fill in the gaps with information we do know.
As the paper states, “GPT-4 is a Transformer-style model pre-trained to predict the next token in a document, using both publicly available data (such as internet data) and data licensed from third-party providers.” (source)
Like it’s previous models, it was trained to predict the next word in a document or from publicly available data.
Another piece of information we glean from this technical analysis is that GPT-4 uses Reinforcement Learning from Human Feedback (RLHF) much like InstructGPT has.
GPT-4 uses RLHF to closely “align” the user’s intent for a given input and helps facilitate trust and safety in its responses.
The table below (sourced from the paper) depicts how GPT-4 performs on a variety of tests:

Additionally, like its predecessors, GPT-4 is able to work with multiple languages and translate between them.
As per the demo, it seems like GPT’s coding ability has been significantly bolstered compared to its predecessors.
Now let’s look at some examples involving visual input (source):

While we don’t have details on the visual architecture, we can see that it is able to take the image and run either of two tasks:
- If the image is a paper or contains text, it’s able to convert the image to text, proceed to understand the context, and finally return a response.
- Otherwise, if the image just contains objects and no text, it’s still able to glean information and return a response, likely still with the use of NLP and language contexting.

Let’s now explore the ways in which GPT-4 differs from GPT-3, including its ability to perform tasks that GPT-3 struggled with, as well as the technical features that make it more robust.
In the demo given by Greg Brockman, President and Co-Founder of OpenAI, the first task that GPT-4 outperformed its predecessor on was summarization.
- Specifically, GPT-4 is able to summarize a corpus with more complex requirements, for example, “Summarize this article but with all words starting with a letter ‘G’”.
In terms of using the model as a coding assistant, you are now able to not only ask it to write code for a specific task, but just copy and paste any errors that code may cause without any context and the model is able to understand and make the code fixes.
One of the coolest tasks that GPT-4 was able to perform was taking a blueprint of a website, hand-drawn in a notebook, and was able to build the entire website in a matter of minutes as the images below show (source):

Additionally, the model is now able to perform really well on academic exams. This shows how much language models have improved in general reasoning capabilities.
“For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%.” (source)

GPT outperforms the previous state-of-the-art models on other standardized exams as well such as: GRE, SAT, BAR, APs as well as other research benchmarks such as: MMLU, HellaSWAG and TextQA (source).

Now, lets look at the technical details of how GPT-4 has outperformed its predecessors.
GPT-4 is capable of handling input context consisting of 8,192 to 32,000 words of text, which means it allows for a longer range of context (~50 pages max).
The image below (source) shows GPT-4 on traditional benchmarks for machine learning models and it is able to outperform existing models as well as most of the SOTA models on most benchmarks.

GPT-4, like its predecessors, still hallucinates facts and make errors in terms of reasoning, thus, the output needs to be verified before it is used from these models.
Much like ChatGPT, GPT-4 lacks knowledge of events that have occurred past the date of its data cut-off, which is September 2021.

Unlike prior GPT family models, we have far less technical details on GPT-4 possibly because it is what powers Bing as confirmed below.

It’ll be fascinating moving forward to see how a multimodal powered search engine can help improve our lives!