ChatGPT predicts the next word in a sequence using a technique called autoregressive language modeling. Here’s how it works:
- Tokenization: When you input text to ChatGPT, it first breaks down the input into smaller units called tokens. These tokens could be words, subwords, or characters, depending on the specific tokenization method used.
- Embedding: Each token is then converted into a high-dimensional vector representation called an embedding. This embedding captures the semantic meaning of the token and its context within the input sequence.
- Model Architecture: ChatGPT utilizes a deep neural network architecture known as a transformer. The transformer model consists of multiple layers of attention mechanisms and feedforward neural networks. These layers allow the model to capture complex patterns and dependencies within the input sequence.
- Autoregressive Generation: To predict the next word in the sequence, ChatGPT uses autoregressive generation. This means that the model generates one token at a time, conditioning its prediction on the tokens that came before it in the input sequence.
- Probability Distribution: For each position in the input sequence, ChatGPT calculates the probability distribution over the entire vocabulary of possible tokens. This distribution represents the likelihood of each token being the next word in the sequence, given the context provided by the preceding tokens.
- Sampling: To generate the next word, ChatGPT samples from this probability distribution. The sampling process introduces a degree of randomness, allowing the model to produce diverse and creative outputs.
- Top-k Sampling: Alternatively, ChatGPT can use top-k sampling, where it considers only the top-k most likely tokens according to the probability distribution. This helps control the randomness of the generated output while still promoting diversity.
- Temperature Scaling: Another technique ChatGPT can use is temperature scaling, which adjusts the steepness of the probability distribution. A higher temperature increases the likelihood of sampling lower-probability tokens, resulting in more diverse but potentially less coherent output.
By combining these techniques, ChatGPT is able to predict the next word in a sequence with a high degree of accuracy, taking into account the context provided by the preceding words.