Technically right in that it doesn’t necessarily translate audio into text, but that’s hardly the point. The point is someone gives the LLM a giant table that directs its response.
it’s a compressed lookup table. rather than there being one response for every input, the input is used as a seed to decompress relevant parts of the dataset, with some added randomness. you can even do it with gzip itself: https://nathan.rs/posts/gzip-lm
It could be represented with a series of lookup tables, especially quantized LLMs. A series of inputs results in a specific output that gets passed to the next set of nodes. Repeat 7 billion times, and the final output from the last set of nodes is a set of token probabilities.
If you are giving the weight matrix to the model yourself, you are doing ML wrong. Machine’s supposed to “learn” the weights itself. That’s the entire point?
That’s a distinction without a difference. The machine doing the inference is not the same machine doing the learning. From the perspective of the machine doing LLM inference, it could not tell you if the table was hand rolled by a human, fitted using ML or just a table of random noise.
Dude… Why do you think the whole point is to have properly tagged data? Or why there were thousands of people working at Amazon Turk categorizing images and files for cents per document?
No. You still have to give it a starting point and the starting point is manually configured tables basically.
And a lot of how it “learns” is by adding more of these into the tables. Every time someone make a video that “brokes” a LLM, these companies will put put those trick questions and screw in the correct answer to train the next generation manually.
Which is why the messing up counting stuffs stayed broken for so long, there’s infinite amount of variations of things that can be counted and validated by a human extremely easily.
I think you grasp the point, it is exactly what an LLM does
An LLM doesn’t transcribe what someone else writes for a given input.
Technically right in that it doesn’t necessarily translate audio into text, but that’s hardly the point. The point is someone gives the LLM a giant table that directs its response.
? A neural network is not a lookup table.
it’s a compressed lookup table. rather than there being one response for every input, the input is used as a seed to decompress relevant parts of the dataset, with some added randomness. you can even do it with gzip itself: https://nathan.rs/posts/gzip-lm
the linked paper is very good: https://arxiv.org/pdf/2309.10668
That’s like saying the neural network in your head has compressed your knowledge of language and just expands the relevant parts when needed.
yup
It could be represented with a series of lookup tables, especially quantized LLMs. A series of inputs results in a specific output that gets passed to the next set of nodes. Repeat 7 billion times, and the final output from the last set of nodes is a set of token probabilities.
If you are giving the weight matrix to the model yourself, you are doing ML wrong. Machine’s supposed to “learn” the weights itself. That’s the entire point?
That’s a distinction without a difference. The machine doing the inference is not the same machine doing the learning. From the perspective of the machine doing LLM inference, it could not tell you if the table was hand rolled by a human, fitted using ML or just a table of random noise.
Dude… Why do you think the whole point is to have properly tagged data? Or why there were thousands of people working at Amazon Turk categorizing images and files for cents per document?
No. You still have to give it a starting point and the starting point is manually configured tables basically.
And a lot of how it “learns” is by adding more of these into the tables. Every time someone make a video that “brokes” a LLM, these companies will put put those trick questions and screw in the correct answer to train the next generation manually.
Which is why the messing up counting stuffs stayed broken for so long, there’s infinite amount of variations of things that can be counted and validated by a human extremely easily.