10 Questions
What is a key innovation of LLMs?
They do not require explicitly labeled data for training.
What type of written material can be used to train LLMs?
Almost any written material, such as Wikipedia, news articles, and computer code.
How does an LLM learn to make better predictions?
By gradually adjusting its weight parameters as it sees more examples.
What does the analogy of the shower faucet represent?
The process of training an LLM from scratch on a large corpus of text.
How many weight parameters does the most powerful version of GPT-3 have?
175 billion
What happens to the weight parameters of an LLM when it is first initialized?
They are set to essentially random numbers.
In the shower faucet analogy, what do the different faucets represent?
The different words in the LLM's vocabulary.
What is the purpose of adjusting the weight parameters during LLM training?
To improve the model's ability to predict the next word in a sequence.
What happens to the adjustments made to the weight parameters as the LLM gets closer to the correct prediction?
The adjustments become smaller and less frequent.
What is the key difference between LLM training and traditional supervised learning?
LLMs do not require explicitly labeled data, while supervised learning does.
Learn about how Large Language Models (LLMs) keep track of information by modifying hidden state vectors as they pass through layers. Explore the use of extremely large word vectors, like the 12,288 dimensions used in GPT-3.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
