Unraveling the Mysteries: A Comprehensive Study of ChatGPT and Its Transformer Backbone - Part 1

This article is composed of three parts and based on my personal research and comprehension of ChatGPT. If there are any aspects that readers believe need to be addressed, updated, or removed, please don’t hesitate to comment and voice your concerns.

Goal:

The objective of this Part-1 article is to offer comprehensive insights into how ChatGPT processes your inquiries and formulates responses. It’s crucial to remember that the essence of a response lies in its accuracy and reliability. Understanding how ChatGPT processes your inquiries and generates highly accurate responses is of utmost importance. This is because, while ChatGPT strives for precision, it can sometimes generate responses that are similar to the examples provided or even produce unexpected results. As such, your feedback and understanding are greatly appreciated in this complex yet fascinating journey of AI communication.

Determine exactly what you want from ChatGPT.

The journey with ChatGPT, a state-of-the-art language model developed by OpenAI, begins with a user’s question or statement. This initial interaction is the starting point for the entire process and sets the stage for the AI’s response. The user’s input could be a simple question, a complex query, or even a request for the AI to perform a specific task. The key is to determine exactly what you want from ChatGPT.

Let’s take an example to illustrate this process. Suppose a user asks, “What is Azure OpenAI?” This question initiates the interaction with ChatGPT and triggers a series of steps that the AI model follows to generate a response.

Let me clarify the four phases shown in the above screenshot.

First Step:

Once you submit your request, there will be a pre-processing being done and under that pre-processing, we have two stages tokenization and padding.

• Tokenization (self-attention): is the process of splitting text into smaller units called tokens, which can be words, characters, subwords, or symbols. Self-attention is a mechanism used in deep learning models to weigh the importance of different tokens in a sequence.
• Padding: is a technique used in machine learning to ensure that all input sequences have the same length by adding special tokens to the end of shorter sequences.

Second Step:

Once tokenization and padding are done, there will be a conversion occurred and the vector conversion is being done by the transformer encoder under step 3.

In a?transformer?architecture, the?encoder?processes the input sequence of tokens and converts them into a sequence of vectors, which can be thought of as a hidden representation of the input sequence.?This is done through a combination of tokenization and vector conversion.

Vector (conversion): is a mathematical representation of data that can be used as input to machine learning models. Vector conversion is the process of converting raw data into a vector representation.

ChatGPT vector numbers have a meaning because they represent the semantic and syntactic features of natural language in a numerical form. Vector numbers are generated by embedding models, such as Word2Vec or GloVe, that learn from large amounts of text data. These models map words, phrases, or sentences to points in a high-dimensional space, where the distance and direction between the points reflect their similarity and relationship. For example, the vector numbers for “cat” and “dog” are closer than the vector numbers for “cat” and “car”.

The Transformer uses vector numbers to encode and decode natural language inputs and outputs, as well as to retrieve relevant information from external sources using vector databases. Vector databases are systems that store and index vector numbers for fast and accurate search and retrieval. ChatGPT can use vector databases to access infinite external memory and augment its generation with factual knowledge. For example, if you ask ChatGPT about the capital of France, it can use a vector database to find the vector number that corresponds to the answer “Paris” and generate a response. Therefore, ChatGPT vector numbers have a meaning because they enable ChatGPT to understand and produce natural language, as well as to interact with other data sources using vector embeddings.

The transformer encoder uses vector numbers? to generate new text and the encoder consists of several layers of self-attention and feed-forward networks that operate on the vector numbers and enrich them with more complex information. The encoder outputs a new vector number per token with the same shape as the input sequence, which is then fed to the decoder for generating the output text.

Third Step:

The Transformer Decoder (generated response) is a type of a deep learning model that generates output data based on the hidden representation generated by the encoder. The decoder consists of several layers of masked self-attention, cross-attention, and feed-forward networks that operate on the vector numbers and produce the next token probabilities. The decoder outputs a new vector number per token with the same shape as the input sequence, which is then fed to a linear layer and a softmax function to obtain the final output of the model.

The masked Self-Attention mechanism allows the decoder to focus on the relevant parts of the input and the previous outputs while preventing it from looking ahead at the future tokens. The cross-attention mechanism allows the decoder to attend to the encoder output and use the context information to generate the output. The feed-forward network applies a non-linear transformation to the vector numbers and adds more complexity to the model. The linear layer and the softmax function project the vector numbers to the vocabulary size and output the probability distribution of the next token. The decoder repeats this process until it generates the end-of-sequence token or reaches the maximum length.

Fourth Step:

Token prediction: In the context of natural language processing, a transformer model equipped with a self-attention mechanism can indeed be viewed as a token prediction tool.

Here’s why: The transformer model processes input tokens (words, characters, or subwords) and uses self-attention to understand the context of each token in relation to the others. Based on this understanding, it predicts the next token in a sequence. This prediction capability is fundamental to how the model generates coherent and contextually relevant text.

For instance, given the sequence “The sky is…”, the model might predict that the next token is “blue” based on the context provided by the previous tokens.

So, in essence, the transformer model’s ability to predict tokens, coupled with its self-attention mechanism, enables it to generate responses that are contextually appropriate and semantically accurate. This is the core of how models like ChatGPT work.

However, it’s important to note that while token prediction is a key function of these models, they also perform many other complex tasks such as encoding and decoding data, managing long-range dependencies in text, and handling nuances in language. So, while it’s accurate to say that a transformer is a token prediction tool, it’s also much more than that. It’s a comprehensive language understanding and generation system.

In summary, token prediction is a key mechanism that enables AI models to understand and generate human language.

Conclusion

Tokenizers constitute an entirely distinct phase in the Language Model (LLM) pipeline. They possess their own unique training set and utilize a specific training algorithm known as Byte Pair Encoding. Once trained, they execute two primary functions: encode(), which transforms strings into tokens, and decode(), which reverts tokens back into strings.

ChatGPT leverages a type of Deep Neural Network (DNN) known as a Transformer model to perform its tasks.

A DNN is indeed an Artificial Neural Network (ANN) with multiple layers between the input and output layers. These intermediate layers are often referred to as hidden layers, and each layer consists of nodes or artificial neurons. These neurons apply different transformations to the input data as it passes through the network, allowing the model to learn complex patterns and relationships in the data.

In the case of ChatGPT, the DNN is used to process the tokenized input text, understand the context, and generate appropriate responses. This involves several steps, including encoding the input data, applying self-attention mechanisms to understand the relationships between different tokens, decoding the encoded data, and predicting the next token in the sequence to generate a response.

Support others by sharing this article.

Thanks a lot for taking the time to read my post. I really value your feedback, so don’t hold back on sharing your thoughts or pointing out anything I might have missed in the first part of this article. If you found this article helpful, feel free to share it with your friends. The second installment of this series of articles can be found right here!

About Me:

Jean Joseph: Data Engineer

As a Microsoft Technical Trainer specializing in Data & AI, I've had the extraordinary opportunity to share my knowledge and passion at numerous prestigious conferences such as Microsoft Build, Databricks Summit, PASS Data Community, SQLBits and others. My expertise has also been sought after by countless user groups on platforms like Meetup, Eventbrite, SQLSaturday, and local user groups in the NJ/NY area.

My daily mission involves enlightening minds in the realm of Data & AI, focusing on cutting-edge services like Azure Synapse Analytics, Microsoft Fabric, Data Science on Azure, Azure OpenAI, Azure AI Search, and Azure Databricks among others. Each day brings a new opportunity to inspire and be inspired, to learn and to teach, to explore the vast expanse of Data & AI. It's not just a job, it's a journey of discovery and innovation.