Warning: these "fundamentals" will become obsolete faster than you can wrap your head around them.
They really don't tho. Transformer was invented in 2017.
Simple Explanation of LLMs
11–20 of 33 posts
Re: Simple Explanation of LLMs
#12It's all prediction. Wolfram has been saying this from the beginning, I think. It hasn't changed and it won't change. But it could be argued that the human mind is fundamentally similar. That consciousness is the combination of a spatial-temporal sense with a future-oriented simulating function. Generally, instead of simulating words or tokens, the biological mind simulates physical concepts. (Needless to say, if you…
With the critical difference that predicting facts and predicting verisimility are massively different operations.
Re: Simple Explanation of LLMs
#13Hello, tried to explain Large Language Models with some visualizations, especially the attention mechanism.
You should probably mention that embeddings are just a renaming of text vectors, aka vector space model, which have probably been used since before neural networks.
Re: Simple Explanation of LLMs
#14It's all prediction. Wolfram has been saying this from the beginning, I think. It hasn't changed and it won't change. But it could be argued that the human mind is fundamentally similar. That consciousness is the combination of a spatial-temporal sense with a future-oriented simulating function. Generally, instead of simulating words or tokens, the biological mind simulates physical concepts. (Needless to say, if you…
With the critical difference that predicting facts and predicting verisimility are massively different operations.
I don't think that anybody predicts "facts" -- there are no oracles, and if you predict a physical concept, it's very easy to get things wrong. Outcomes are, in some cases, almost statistical.
(A physical concept could be something as simple as how to catch a frisbee, or, alternatively, imagine a cat trying to predict how best to swipe at a fleeing mouse. If the mouse zigs when it could have zagged, the cat, for all its well-honed instincts, may miss. It may have predicted wrongly.)
Predicting tokens is really quite similar. I really think that it's the same type of thing.
Getting facts right is a matter of error correction and knowledgebase utilization, which is why "reasoning models" with error correction layers and RAG are so good.
Re: Simple Explanation of LLMs
#15Would love to see a similar explanation of how "reasoning" versions of LLMs are trained. I understand that OpenAI was mum about how they specifically trained o1/o3 and that people are having to reverse engineer from the DeepSeek paper which may or may not be a different approach, but would like to see a coherent explanation which is not just an regurgitation of Chain of Thought or handwavy "special reasoning tokens g…
This may be useful: https://www.interconnects.ai/p/deepseek-r1-recipe-for-o1 but the tl;dr of the idea is that we can use reinforcement learning on a strong base model (i.e. one that hasn't been fine tuned) to elicit the generation of tokens that help the model reach a result that can be verified to be correct. That is, if we have a way of verifying that a specific output is correct, the model can be trained to consi…
This paper [1] even claims that "models primed with incorrect solutions containing proper reasoning patterns achieve comparable performance to those trained on correct solutions."
Re: Simple Explanation of LLMs
#16Warning: these "fundamentals" will become obsolete faster than you can wrap your head around them.
It would be nice if there was a place (e.g. github repo) that tracked the best resources for learning this stuff.
Re: Simple Explanation of LLMs
#17[deleted]
Re: Simple Explanation of LLMs
#18Earlier quoted context omitted.
With the critical difference that predicting facts and predicting verisimility are massively different operations.
I don't think that anybody predicts "facts" -- there are no oracles, and if you predict a physical concept, it's very easy to get things wrong. Outcomes are, in some cases, almost statistical. (A physical concept could be something as simple as how to catch a frisbee, or, alternatively, imagine a cat trying to predict how best to swipe at a fleeing mouse. If the mouse zigs when it could have zagged, the cat, for all…
> there are no oracles
If you mean "guessing without grounds", that is exactly the phenomenon which is expressed by bad thinkers in both the carbon and the silicon realms, and that is what we are countering.
> predict[ing] "facts"
It's called "Science". In a broader way, it's called "intelligence" ("Intelligence is being able to predict the outcomes of an experience you never had" ~~ Prof. Patrick Winston)
> Getting facts right is a matter of
It is a matter of procedurally adhering to an attitude of iterative quality refinement of ideas, and LLMs seem to be dramatically bad at "procedures".
Re: Simple Explanation of LLMs
#19Earlier quoted context omitted.
I don't think that anybody predicts "facts" -- there are no oracles, and if you predict a physical concept, it's very easy to get things wrong. Outcomes are, in some cases, almost statistical. (A physical concept could be something as simple as how to catch a frisbee, or, alternatively, imagine a cat trying to predict how best to swipe at a fleeing mouse. If the mouse zigs when it could have zagged, the cat, for all…
> there are no oracles If you mean "guessing without grounds", that is exactly the phenomenon which is expressed by bad thinkers in both the carbon and the silicon realms, and that is what we are countering. > predict[ing] "facts" It's called "Science". In a broader way, it's called "intelligence" ("Intelligence is being able to predict the outcomes of an experience you never had" ~~ Prof. Patrick Winston) > Getting…
When you say "predicting facts" you imply "predicting true future events." Delphi is no longer operational, so it simply can't be done. (At least, not past a certain -- very, very low -- complexity threshold in the macroscopic non-quantum world.)
"Science" is coming up with, and testing, theories -- they may be true, they may be false, and you can't know, and shouldn't hold a very strong position, until you test them. It's true that a more intelligent person will come up with better hypotheses and more inventive ways to put them to the test, but that's not what you seemed to be talking about, nor are we in any disagreement on that point.
A more intelligent cat will also catch mice more effectively -- it'll have a more accurate mental model of the mouse and of its own physical capabilities in time and space. Still, the outcome of the hunt is never perfectly predictable. Some outcomes are statistical -- and, intriguingly, LLMs mirror this in how they predict tokens.
> LLMs seem to be dramatically bad at "procedures".
How do you figure, and how did you reach this conclusion?
Re: Simple Explanation of LLMs
#20It's all prediction. Wolfram has been saying this from the beginning, I think. It hasn't changed and it won't change. But it could be argued that the human mind is fundamentally similar. That consciousness is the combination of a spatial-temporal sense with a future-oriented simulating function. Generally, instead of simulating words or tokens, the biological mind simulates physical concepts. (Needless to say, if you…
> Wolfram has been saying this from the beginning, I think.
Wolfram has been distinguishing between probabilistic output and deterministic output from a neural network since the beginning? Trying to monopolize on such basic concepts doesn't make much sense. It's like saying he has been thinking of sporks since the beginning.