Large Language Models explained briefly
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