Source Material: Stephen Wolfram, 2023-02-14
ChatGPT is a large-scale transformer-based language model that is designed to predict the next word in a sentence given the context of what has been said. It is a neural network with 175 billion parameters that has been trained on a vast corpus of text, enabling it to form and apply a semantic structure to human language.
The name ChatGPT stands for Generative Pre-trained Transformer. Generative means that the model is capable of generating new text rather than just recognizing patterns in existing text. Pre-trained means that the model has been trained on a large corpus of text before being fine-tuned for a specific task. Transformer refers to the specific type of neural network architecture which is designed to better handle long-term dependencies between words in a sentence.
To accomplish its task, ChatGPT uses a technique known as unsupervised learning, which allows it to learn patterns in the data without being explicitly taught. Instead of being trained on explicit examples of inputs and their associated outputs like in supervised learning, the model is given a large corpus of text and is trained to predict the next word in a sentence by masking the latter part of the sentence and having it predict what should come next. It then compares what it generated with the masked text, and iteratively adjusts its parameters to minimize the error.
To evaluate how well the model performs on each iteration, a loss function is used. The loss function calculates how far away the model’s predictions are from the desired outcome, and the neural net weights are adjusted in a way that minimizes the result of the loss function.
Training the model both optimizes the neural net weights and produces embeddings, which are a way of representing the meaning of words as arrays of numbers (in the vague, undefinable sense of ‘meaning’). Nearby words are represented by nearby numbers. ChatGPT takes this concept further by generating embeddings not just for individual words, but for entire sequences of words.
These embeddings are then used to predict the probabilities of different words that might come next in a sentence. This is accomplished using a transformer architecture, which is designed to better handle dependencies between tokens in the input and output even when they are far from each other in the input sequence. One of the defining features of the transformer is its use of an attention mechanism, which involves certain neurons focusing more on relevant parts of the sequence than others. This allows ChatGPT to take into account the context of the conversation that’s taken place, which can inform the next token that’s generated. The attention mechanism also allows ChatGPT to capture context from the prior conversation even when that context is not adjacent to the token being generated. This is the main reason ChatGPT comes across as a coherent entity.
Finally, ChatGPT uses a temperature setting to introduce a degree of randomness into its predictions, which can make the output more diverse and interesting.
What strikes me as the most profound point Stephen makes is the success of ChatGPT as a scientific discovery in that it shows that there may be simple rules that describe how the semantics of human language can be arranged that we ourselves don’t yet understand. Studying the pathways and structures ChatGPT uses could help deepen our own understanding of human language.