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this post was submitted on 31 Aug 2023
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For the AI heads here: is this another problem caused by the "black box" style of LLM creation where they don't really know how it actually works, so they don't really know how to take out the data?
They know how it works. It's a statistical model. Given a sequence of words, there's a set of probabilities for what the next word will be. That's the problem, an LLM doesn't "know" anything. It's not a collection of facts. It's like a pachinko machine where each peg in the machine is a word. The prompt you give it determines where/how the ball gets dropped in and all the pins it hits on the way down corresponds to the output. How those pins get labeled is the learning process. Once that's done there really isn't any going back. You can't unscramble that egg to pick out one piece of the training data.
That is a gross oversimplification. LLM's operate on much more than just statistical probabilities. It's true that they predict the next word based on probabilities learned from training datasets, but they also have layers of transformers to process the context provided from a prompt to eke out meaningful relationships between words and phrases.
For example: Imagine you give an LLM the prompt, "Dumbledore went to the store to get ice cream and passed his friend Sam along the way. At the store, he got chocolate ice cream." Now, if you ask the model, "who got chocolate ice cream from the store?" it doesn't just blindly rely on statistical likelihood. There's no way you could argue that "Dumbledore" is a statistically likely word to follow the text "who got chocolate ice cream from the store?" Instead, it uses its understanding of the specific context to determine that "Dumbledore" is the one who got chocolate ice cream from the store.
So, it's not just statistical probabilities; the models' have an ability to comprehend context and generate meaningful responses based on that context.