How ChatGPT Works: The Model Behind The Bot
A brief introduction to the intuition and methodology behind the chat bot you can’t stop hearing about.
This gentle introduction to the machine learning models that power ChatGPT, will start at the introduction of Large Language Models, dive into the revolutionary self-attention mechanism that enabled GPT-3 to be trained, and then burrow into Reinforcement Learning From Human Feedback, the novel technique that made ChatGPT exceptional.
Large Language Models
ChatGPT is an extrapolation of a class of machine learning Natural Language Processing models known as Large Language Model (LLMs). LLMs digest huge quantities of text data and infer relationships between words within the text. These models have grown over the last few years as we’ve seen advancements in computational power. LLMs increase their capability as the size of their input datasets and parameter space increase.
The most basic training of language models involves predicting a word in a sequence of words. Most commonly, this is observed as either next-token-prediction and masked-language-modeling.
In this basic sequencing technique, often deployed through a Long-Short-Term-Memory (LSTM) model, the model is filling in the blank with the most statistically probable word given the surrounding context. There are two major limitations with this sequential modeling structure.
- The model is unable to value some of the surrounding words more than others. In the above example, while ‘reading’ may most often associate with ‘hates’, in the database ‘Jacob’ may be such an avid reader that the model should give more weight to ‘Jacob’ than to ‘reading’ and choose ‘love’ instead of ‘hates’.
- The input data is processed individually and sequentially rather than as a whole corpus. This means that when an LSTM is trained, the window of context is fixed, extending only beyond an individual input for several steps in the sequence. This limits the complexity of the relationships between words and the meanings…
