At its core, ChatGPT is a deep neural network trained on vast amounts of conversational data. Given an input sequence, the model encodes it with the aid of self-attention mechanisms and decodes the resulting representation to generate a response through a language generation model.
Natural Language Processing, or NLP, is a subfield of AI concerned with enabling computers to understand, analyse, and generate human language. NLP encompasses a range of tasks, from text pre-processing, tokenisation, and part-of-speech tagging, to parsing, named entity recognition, sentiment analysis, and text generation. The NLP process utilises both rule-based and statistical methods, trained on large annotated text corpora through supervised, unsupervised, and reinforcement learning techniques.
When generating its responses, ChatGPT leverages the probabilities assigned to sequences of words. The model first encodes the input text to capture its meaning and context and then generates a response by determining the next word in the sequence based on the input context, and the patterns learned during training.
The response is generated by sampling the most likely words at each step and combining them into a coherent and relevant response, guided by the goal of maximising the likelihood of generating a suitable response.