Comment on Study finds that Chat GPT will cheat when given the opportunity and lie to cover it up later.
SmoothIsFast@citizensgaming.com 11 months agoDo you understand how they work or not? First I take all human text online. Next, I rank how likely those words come after another. Last write a loop getting the next possible word until the end line character is thought to be most probable. There you go that’s essentially the loop of an LLM. There are design elements that make creating the training data quicker, or the model quicker at picking the next word but at the core this is all they do.
It makes sense to me to accept that if it looks like a duck, and it quacks like a duck, then it is a duck, for a lot (but not all) of important purposes.
I.e. the only duck it walks and quacks like is autocomplete, it does not have agency or any other “emergent” features. For something to even have an emergent property, the system needs to have feedback from itself, which an LLM does not.
froop@lemmy.world 11 months ago
Your description is how pre-llm chatbots work. They were really bad, obviously. It’s overly simplified to the point of dishonesty for llms though.
Emergent properties don’t require feedback. They just need components of the system to interact to produce properties that the individual components don’t have. The llm model is billions of components interacting in unexpected ways. Emergent properties are literally the only reason llms work at all. So I don’t think it’s absurd to think that the system might have other emergent properties that could be interpreted to be actual understanding.
SmoothIsFast@citizensgaming.com 11 months ago
Not really we just parallelized the computing and used other models to filter our training data and tokenize them. Sure the loop looks more complex because of parallelization and tokenizing the words used as inputs and selections, but it doesn’t change what the underlying principles are here.
Yes they need proper interaction, or you know feedback for this to occur. Glad we covered that. Having more items but gating their interaction is not adding more components to the system, it’s creating a new system to follow the old. Which in this case is still just more probability calculations. Sorry, but chaining probability calculations is not gonna somehow make something sentient or aware. For that to happen it needs to be able to influence its internal weighting or training data without external aid, hint these models are deterministic meaning no there is zero feedback or interaction to create Emergent properties in this system.
No llms work because we massively increased the size and throughput of our probability calculations, allowing increased precision on the predictions, which means they look more intelligible. That’s it. Garbage in garbage out still applies, and making it larger does not mean that this garbage is gonna magically create new control loops in your code, it might increase precision as you have more options to compare and weight against but it does not change the underlying system.
froop@lemmy.world 11 months ago
The interaction is between nodes in the model. Those are the components that individually have no real characteristics, but when combined into a billion-dimension model, that results in emergent properties. Correctly writing novel code is an emergent property. Correctly solving an ASCII art maze is an emergent property. There is a point where a text predictor, being sufficiently accurate, demonstrates emergent understanding.
Your definition emergent property is outright wrong.