Tada! Here's Part 3 of the large language model series!
Are you excited for the next chapter of our LLM 101 series? So far we've covered tokenization and encoding. If you need a refresher, have a look at the pages below.
In short, we've learnt how to break down pieces of text and assign a number to each piece. This ensures that the model will "know" what your question is going to be. But how do these models answer back in a way humans would understand? This is where the concept of decoding is important.
If encoding is to turn text into numbers, decoding is the reverse: to turn numbers into text. Here's a PDF of a Jupyter Notebook which allows us to revise the previous concepts as well as show what decoding looks like in code.
Now that you've had a look at the PDF, we've now covered tokenization (breaking down text to pieces), encoding (converting pieces of text to numbers), and decoding (converting numbers back to text).
You should now be able to:
- Type a text-based input into the model
- Retrieve text back from the model
I know it doesn't sound like you're doing much at the moment but nailing these concepts will help you build and use LLMs in the long run. Next time, we'll be asking the model to perform simple tasks using Python. Namely, summarization, paraphrasing, and translation.
Ending
How have you found the LLM series so far? Hopefully it's been helpful in understanding these basic concepts. I remember being pretty lost when I first started studying about LLMs, so I'm aiming to make LLM studies more accessible for all!!! It may be easier to use ChatGPT to generate answers (especially since some of the earlier versions are available for free now) but I found that being able to use LLM using code is very satisfying. 💪
