capacity to store more patterns about language learned during training

Moment at 5:44 in Large Language Models explained briefly by 3Blue1Brown.

Large Language Models explained briefly

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• Imagine you happen across a short movie script that
• describes a scene between a person and their AI assistant
• The script has what the person asks the AI, but the AI's response has been torn off
• Suppose you also have this powerful magical machine that can take
• any text and provide a sensible prediction of what word comes next
• You could then finish the script by feeding in what you have to the machine
• seeing what it would predict to start the AI's answer
• and then repeating this over and over with a growing script completing the dialogue
• When you interact with a chatbot, this is exactly what's happening
• A large language model is a sophisticated mathematical function
• that predicts what word comes next for any piece of text
• Instead of predicting one word with certainty, though
• what it does is assign a probability to all possible next words
• To build a chatbot, what you do is lay out some
• text that describes an interaction between a user
• and a hypothetical AI assistant, you add on whatever
• the user types in as the first part of
• the interaction, and then have the model repeatedly predict the next word that such a
• hypothetical AI assistant would say in response, and that's what's presented to the user
• In doing this, the output tends to look a lot more natural if
• you allow it to select less likely words along the way at random
• So what this means is even though the model itself is deterministic
• a given prompt typically gives a different answer each time it's run
• Models learn how to make these predictions by processing an enormous amount of text
• typically pulled from the internet
• For a standard human to read the amount of text that was used to train GPT-3
• for example, if they read non-stop 24-7, it would take over 2600 years
• Larger models since then train on much, much more
• You can think of training a little bit like tuning the dials on a big machine
• The way that a language model behaves is entirely determined by these
• many different continuous values, usually called parameters or weights
• Changing those parameters will change the probabilities
• that the model gives for the next word on a given input
• What puts the large in large language model is how
• they can have hundreds of billions of these parameters
• No human ever deliberately sets those parameters
• Instead, they begin at random, meaning the model just outputs gibberish
• but they're repeatedly refined based on many example pieces of text
• One of these training examples could be just a handful of words
• or it could be thousands, but in either case, the way this works is to
• pass in all but the last word from that example into the model and
• compare the prediction that it makes with the true last word from the example
• An algorithm called backpropagation is used to tweak all of the parameters
• in such a way that it makes the model a little more likely to choose
• the true last word and a little less likely to choose all the others
• When you do this for many, many trillions of examples
• not only does the model start to give more accurate predictions on the training data
• but it also starts to make more reasonable predictions on text that it's never
• seen before
• Given the huge number of parameters and the enormous amount of training data
• the scale of computation involved in training a large language model is mind-boggling
• To illustrate, imagine that you could perform one
• billion additions and multiplications every single second
• How long do you think it would take for you to do all of the
• operations involved in training the largest language models?
• Do you think it would take a year?
• Maybe something like 10,000 years?
• The answer is actually much more than that
• It's well over 100 million years
• This is only part of the story, though
• This whole process is called pre-training
• The goal of auto-completing a random passage of text from the
• internet is very different from the goal of being a good AI assistant
• To address this, chatbots undergo another type of training
• just as important, called reinforcement learning with human feedback
• Workers flag unhelpful or problematic predictions
• and their corrections further change the model's parameters
• making them more likely to give predictions that users prefer
• Looking back at the pre-training, though, this staggering amount of
• computation is only made possible by using special computer chips that
• are optimized for running many operations in parallel, known as GPUs
• However, not all language models can be easily parallelized
• Prior to 2017, most language models would process text one word at a time
• but then a team of researchers at Google introduced a new model known as the transformer
• Transformers don't read text from the start to the finish
• they soak it all in at once, in parallel
• The very first step inside a transformer, and most other language models for that matter
• is to associate each word with a long list of numbers
• The reason for this is that the training process only works with continuous values
• so you have to somehow encode language using numbers
• and each of these lists of numbers may somehow encode the meaning of the
• corresponding word
• What makes transformers unique is their reliance
• on a special operation known as attention
• This operation gives all of these lists of numbers a chance to talk to one another
• and refine the meanings they encode based on the context around, all done in parallel
• For example, the numbers encoding the word bank might be changed based on the
• context surrounding it to somehow encode the more specific notion of a riverbank
• Transformers typically also include a second type of operation known
• as a feed-forward neural network, and this gives the model extra
• capacity to store more patterns about language learned during training
• All of this data repeatedly flows through many different iterations of
• these two fundamental operations, and as it does so
• the hope is that each list of numbers is enriched to encode whatever
• information might be needed to make an accurate prediction of what word
• follows in the passage
• At the end, one final function is performed on the last vector in this sequence
• which now has had a chance to be influenced by all the other context from the input text
• as well as everything the model learned during training
• to produce a prediction of the next word

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