ChatGPT: How a Language Model is generating response base on text input!

In recent years, language models have become increasingly popular for a wide range of natural language processing tasks, from text classification to machine translation. Among these models, the GPT family of models, based on the Transformer architecture, has emerged as a leader in the field.

ChatGPT is a language model that uses a variant of the transformer architecture called the GPT (Generative Pre-trained Transformer) architecture. The GPT architecture is a deep neural network consisting of multiple layers of self-attention and feed-forward networks.

At its core, ChatGPT is a language model that has been trained on massive amounts of text data from the internet. By analyzing this data, the model has learned to understand the underlying patterns and structure of language, allowing it to generate human-like responses to a wide range of prompts and queries. This capability has made ChatGPT a valuable tool for a wide range of applications, from customer service chat bots to personalized virtual assistants.

So, what is Transformer architecture?

The Transformer architecture is a type of neural network architecture that has been widely used for natural language processing tasks, including machine translation, language modeling, and text generation. It was first introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017.

The Transformer architecture is based on the concept of self-attention, which allows the model to focus on different parts of the input sequence when generating its output. In a traditional neural network architecture, information flows sequentially through the layers of the network, with each layer processing the output of the previous layer. In contrast, the Transformer architecture allows information to flow in a more parallel and flexible way, enabling the model to capture complex dependencies and relationships between different parts of the input sequence.

The Transformer architecture also includes a series of feed-forward neural networks and normalization layers, which allow the model to refine its understanding of the input and generate increasingly accurate embedding that capture the semantic and syntactic properties of the language. Additionally, the architecture includes techniques such as residual connections and layer normalization, which help to mitigate the problem of vanishing gradients and improve the stability and performance of the model.

Overall, the Transformer architecture has proven to be a highly effective and versatile approach for natural language processing tasks. Its ability to capture long-range dependencies and relationships between different parts of the input sequence has made it a powerful tool for tasks such as machine translation, language modeling, and text generation.

How it process text input to generate response?

Lets take a example, “Who was the first president of United states ?” as question to chatGPT.

When you input a message to ChatGPT, it first tokenizes the input message into a sequence of tokens (usually words) and converts these tokens into numerical representations using an embedding layer. The resulting embedding are then fed into the first layer of the GPT architecture.

So for above example, the input prompt would be tokenized into a sequence of tokens, such as ["who", "was", "the", "first", "president", "of", "United", "state", "?"]. These tokens would then be converted into numerical representations using an embedding layer.

During training, the GPT model learns to compute attention weights based on the similarity between the embeddings of each token in the sequence. Tokens that are semantically similar or relevant to the input prompt are likely to receive higher attention weights, while tokens that are less relevant or redundant are likely to receive lower attention weights.

Next, the embeddings would be fed into the first layer of the GPT architecture. In this layer, self-attention mechanisms would be used to weigh the importance of each token in the input sequence with respect to the other tokens in the same sequence. For example, the self-attention mechanism might give a high weight to the "president" token because it is directly related to the question being asked, while giving a lower weight to the "was" token because it is less important for answering the question.

The resulting weighted sum of the input embedding would then be passed through a feed-forward network, which would apply non-linear transformations to the embedding to produce a new set of embedding. These new embedding would be fed into the next layer of the GPT architecture, and the process would repeat for several layers.

After processing all the layers, the final output of the GPT model would be a probability distribution over all possible tokens in the vocabulary. In this case, the GPT model would likely give high probabilities to tokens such as "George", "Washington", and "first" because they are related to the prompt and the question being asked.

Finally, ChatGPT would generate a response by sampling from this probability distribution, choosing the token with the highest probability at each step until a stopping criterion is met. The stopping criterion is typically a per-defined maximum length for the response, or a special token indicating the end of the response. For example, in the case of the input prompt "who was the first president of United state?", ChatGPT might use a stopping criterion of a maximum response length of 20 tokens, or a special end-of-sentence token such as "." or "?" and ChatGPT might generate a response like "The first president of the United States was George Washington."

During the generation process, ChatGPT may use techniques such as top-k sampling or nucleus sampling to introduce randomness into the output and avoid generating repetitive or uninteresting responses. These techniques involve sampling from a subset of the highest-probability tokens or a subset of the probability distribution with a cumulative probability mass within a per-defined threshold, respectively.

Overall, the generation process is designed to produce responses that are fluent, relevant, and diverse, while also adhering to the constraints imposed by the input prompt and the stopping criterion.

-Manish Joshi (Senior Software Engineer)