How does an LLM like ChatGPT actually work?

Hi -,

Wishing you great start to your summer! Today I want to do two things:

1. Offer a beginner lesson on a topic everyone should be aware of: Large Language Models (LLMs) 
2. Share a quick quiz to test your knowledge

But first...

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📚 Topic of the Week:

The Basics of Large Language Models (LLMs)

Imagine a conversation with a jewelry maker. They start a sentence with, "I'm a jewelry maker, so I'm going to make a pair of ..."

You'd likely predict that the next word is "earrings," right?

Large language models (LLMs) work in a similar way. LLM is a type of artificial intelligence that can understand and generate human-like text. 

(FYI ChatGPT is a great example of an LLM that you've probably heard of).

Just as you used your knowledge about jewelry makers to guess "earrings," LLM uses its vast knowledge to generate human-like responses. It looks at the words that came before and makes an informed prediction about what should follow, allowing it to understand and create text on almost any topic.

Let's say you want your AI to be a knowledgeable expert in jewelry making.  Fine-tuning is the process of adjusting this AI to enhance its performance using relevant data such as industry-specific terms, techniques, and trends. This involves training the LLM on a specialized dataset that includes detailed information about jewelry making.

As a result, the fine-tuned model becomes much better at understanding and generating text related to jewelry making, just like a human expert in the field would.

Learning how to fine-tune models can help individuals adapt powerful LLMs for their specific use cases. 

Here's a picture to demonstrate various uses of fine-tuning: 

Fine-tuning is a crucial step to ensure that broad AI models are in alignment with the specific needs of the users. 

How to fine-tune LLMs? 

Fine-tuning a language model is like teaching a smart robot new tricks. Here's how: 

1. Pick a pre-trained model. (This is like choosing a robot that already possesses a wide-range understanding of language)
2. Choose what you want to teach it. (Maybe you want it to understand advanced jewelry-making terms)
3. Collect lots of examples of the task you want it to learn.
4. Train the model by showing the examples over and over.
5. Check and adjust to see how well it's doing and make changes if needed.
6. Repeat steps 4 and 5.

Remember, it's okay if this seems tricky at first. Even experienced coders are still learning how to do this well!

🧠 Finally, here's your coding quiz of the week...

True or False:

1. LLM's are trained to predict the next word by looking at the words that came after it. (T/F)

2. Learning to fine-tune models can help you adapt powerful LLMs for specific use cases. (T/F)

3. When you're fine-tuning LLM's, you must train the model by showing examples over and over as a first step. (T/F)

(Answers are at the bottom!)

Good luck and happy learning!

🙊 Answers: F,T,F

Fahim ul Haq
CEO & Cofounder
educative.io