ChatGPT: a deep dive
The ChatGPT (Chat Generative Pre-trained Transformer) is a large-scale language model developed by OpenAI. It is trained on a massive dataset of text and can generate human-like text on a wide range of topics. The motivation behind developing such a model is to enhance the capabilities of machine-generated text, making it more human-like, and thus more useful in a wide range of applications.
First introduced in June 2020, as an improvement over its predecessor GPT-2, which was released in February 2018.
GPT-3 was released on November 30, 2022, and it has received a lot of attention from researchers, industry experts, and media outlets, due to its advanced capabilities and potential for future applications. GPT-3 is the latest version of the GPT (Generative Pre-trained Transformer) series of models developed by OpenAI.
The model is trained on 570 GB of text data, which includes a diverse range of books, articles, and websites. It has incredible 175B parameters.
The model is notable for its ability to perform a wide range of natural language processing (NLP) tasks, such as language translation, text summarization, and question-answering.
Exponential Growth
The growth of ChatGPT has been nothing short of phenomenal. In just five days, the platform has amassed an impressive 5 million users. To put this in perspective, it took industry giants like Netflix 3.5 years, Airbnb 2.5 years, and Twitter 2 years to reach the same milestone.
This exponential growth is a testament to the power and potential of ChatGPT as a language model. The ability to generate human-like text and its versatility in various NLP tasks have resonated with a wide range of users, and this is reflected in the platform’s rapid user growth. As the world becomes increasingly reliant on technology and automation, ChatGPT is poised to play a significant role in shaping the future of natural language processing.
Technical overview
ChatGPT combines multiple AI techniques to generate coherent and fluent text. During the training process, the model learns to predict the next word in a sentence given the context of the words that come before it.
It relies on a multi-layer architecture: ChatGPT’s core is based on transformer architecture and uses a deep neural network with the attention mechanism.
- The transformer architecture is responsible for serialising the text into continuous vectors that will be processed by the model
- Attention Mechanism: set the importance or the priority of the different words from the input.
- Human feedback: As we have in Google Search, by giving feedback, the model is trained in real-time. This is under the reinforcement learning umbrella.
The Transform architecture and the attention mechanism were introduced in 2017 by Google, in the paper called Attention Is All You Need.
Transformer architecture
A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing (NLP) and computer vision (CV).
Like recurrent neural networks (RNNs), transformers are designed to process sequential input data, such as natural language, with applications towards tasks such as translation and text summarization. However, unlike RNNs, transformers process the entire input all at once. The attention mechanism provides context for any position in the input sequence. For example, if the input data is a natural language sentence, the transformer does not have to process one word at a time. This allows for more parallelization than RNNs and therefore reduces training times.
Transformers were introduced in 2017 by a team at Google Brain and are increasingly the model of choice for NLP problems, replacing RNN models such as long short-term memory (LSTM). The additional training parallelization allows training on larger datasets. This led to the development of pre-trained systems such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), which were trained with large language datasets, such as the Wikipedia Corpus and Common Crawl, and can be fine-tuned for specific tasks.
The diagram below gives us a general idea of how it works:
Input
The input text is parsed into tokens by a byte pair encoding tokenizer, and each token is converted via a word embedding into a vector. Then, the positional information of the token is added to the word embedding.
Encoder–decoder architecture
The Transformer model, like earlier seq2seq models, utilizes an encoder-decoder architecture. The encoder is made up of multiple layers that process the input one after the other, and the decoder performs the same function on the encoder’s output.
The encoder layers generate encodings that contain information about how different parts of the input are related to each other, and they pass these encodings to the next encoder layer as input. The decoder layers, on the other hand, take all the encodings and use the contextual information they contain to generate an output sequence.
To accomplish this, each encoder and decoder layer employs an attention mechanism. Attention assigns relevance to different parts of the input and uses that information to generate the output. The decoder layers also have an additional attention mechanism that draws information from the outputs of previous decoder layers before drawing information from the encodings.
Both the encoder and decoder layers contain a feed-forward neural network for additional processing of the outputs, and they also have residual connections and layer normalization steps.
Attention is all you need
In artificial neural networks, attention is a technique that is meant to mimic cognitive attention. The effect enhances some parts of the input data while diminishing other parts — the motivation being that the network should devote more focus to the small, but important, parts of the data. Learning which part of the data is more important than another depends on the context, and this is trained by gradient descent.
In simple terms, the attention mechanism allows the model to weigh the importance of different words in the input text and generate a weighted representation of the input.
Imagine you are reading a book and you want to remember a specific sentence, you would focus your attention on that sentence, in the same way, the attention mechanism allows the model to focus on specific words in the input text and understand their context better.
The attention mechanism works by calculating the similarity between different words in the input text and then assigning a weight to each word based on its similarity to the other words. This weighted representation is then used to generate the final output, which is a probability distribution over all possible next words in the vocabulary.
A language translation example:
To build a machine that translates English to French, one takes the basic Encoder-Decoder and graft an attention unit to it (diagram below). In the simplest case, the attention unit consists of dot products of the recurrent encoder states and does not need training. In practice, the attention unit consists of 3 fully-connected neural network layers called query-key-value that need to be trained.
Let’s see the animation below, with the step-by-step process:
Encoder-decoder with attention. The left part (black lines) is the encoder-decoder, the middle part (orange lines) is the attention unit, and the right part (in grey & colours) is the computed data. Grey regions in the H matrix and w vector are zero values. Numerical subscripts indicate vector sizes while lettered subscripts i and i − 1 indicate time steps.
Human feedback
Human feedback is a game changer in the context of language models like GPT-3 because it allows the model to learn from and adapt to the nuances of human language in real-time.
- Improving the model’s accuracy: By receiving feedback from humans, the model can learn from its mistakes and improve its accuracy in generating human-like text.
- Adapting to new language patterns: Human feedback can help the model to understand new language patterns and idioms, which may not be present in the training data.
- Personalizing the model: By receiving feedback from a specific user, the model can learn to adapt to that user’s writing style and preferences.
- Enhancing the model’s performance: Human feedback can also be used to fine-tune the model’s parameters and enhance its performance on specific tasks.
- Avoiding (?) biases: Human feedback can help the model to avoid biases that may be present in the training data, and make the model’s output more diverse and inclusive.
Use cases
ChatGPT is a powerful language model that can be used for a variety of natural language processing (NLP) tasks such as text generation, language translation, and text summarization. It is particularly useful for generating human-like text, making it useful in applications such as chatbots, virtual assistants, and automated customer service. Additionally, ChatGPT can be fine-tuned for specific use cases such as product descriptions, news articles, and creative writing, making it a versatile tool for content creation. Its ability to generate text that is coherent, fluent, and contextually appropriate makes it a valuable asset for businesses, organizations and researchers in the field of NLP.
Hereby are some examples:
1. Power up Microsoft Excel
2. Simple English to SQL
3. English to Deep Learning Model, using Keras
And some funny and random stuff:
Your wife is always right
Bitcoin by Donald Trump
Wrap-up
In conclusion, ChatGPT is a powerful language model that has seen exponential growth in a short period of time. Its ability to generate human-like text and its versatility in various NLP tasks have made it a valuable asset for businesses, organizations and researchers in the field of NLP. The transformer and attention architectures that underlie ChatGPT are what makes it such a powerful tool, allowing it to analyze and understand the context in a way that was not possible before.
Recently, OpenAI announced a partnership with Microsoft, with an additional investment of $10 billion. This partnership has caused quite a stir in the tech industry. The New York Times reported that it has led Google to declare a “code red” over fears that it might enable competitors to eat into the firm’s $149 billion search business. The reason for this concern is that large language models like OpenAI’s ChatGPT might change the way we search the internet forever. Not only that, but it might also change how we study and are assessed. These are just a few examples of the many ways in which these powerful language models are expected to disrupt and change various industries.
We’ve seen examples of its use cases, from coding generation to virtual assistants and others. We’ve also seen some funny examples of what ChatGPT can do, such as writing jokes.
But this is just the beginning. As the world becomes increasingly reliant on technology and automation, the potential for ChatGPT and other large language models is endless. From natural language understanding to text-to-speech, to language translation, the future looks bright for the field of NLP and the role that ChatGPT will play in shaping it.
I encourage you to explore and experiment with ChatGPT to see all the possibilities it can offer. Also, if you already did, share your experience in the comments section below.
See you next time!
