LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
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To start, everything you're saying is entirely correct
However, the existence of emergent behaviours like chain of thought reasoning shows that there's more to this than pure text predictions, it picks up patterns that were never explicitly trained, so it's entirely feasible to ponder if they're able to recognize reverse patterns
Hallucinations are a vital part of understanding the models, they might not be long term problems but getting them to understand what they actually know to be true is extremely important in the growth and adoption of LLMs
I think there's a lot more to the training and generation of text than you're giving it credit, the simplest way to explain it is that it's text prediction, but there's way too much depth to the training and model to say that's all it is
At the end of the day it's just a fun thought inducing post :) but when Andrej karparthy says he doesn't have a great intuition on how LLM knowledge works (though in fairness he theorizes the same as you, directional learning) I think we can at least agree none of us know for sure what is correct!