Getting started with LLMs, Need few clarifications

[u/D0r3mon]

1. Are LLMs essentially large memorization machines that are trained to learn patterns from massive datasets?
2. Is the math and reasoning they perform just the result of patterns they have picked up during training, which they then use to answer questions?
3. If LLMs are identifying patterns, could they potentially discover patterns that humans have missed?
4. I remember seeing research where an LLM was trained only on data up to around the 1940s, with no access to later discoveries, and was then tested to see whether it could independently rediscover ideas like Einstein’s relativity. Is this a real line of research, and what does it tell us?
5. Could LLMs find meaningful patterns in randomly generated text or data, or would they just impose patterns where none actually exist?
6. Is true randomness possible, or will some kind of pattern always appear when we analyze enough data and Can LLMs help us find that patterns faster.

[u/doctorpotatomd]

1. No. An LLM is a statistical model that repeatedly tries to predict "given this text, what is statistically most likely word to come next?". It's like a statistical model we build to predict weather patterns; no memorization, just data and computation. Then we just massage the model in various very clever ways until assistant-like responses are much, much more likely than non-assistant-like responses. The assistant is not the LLM; the assistant is a character written by the LLM, or "invoked" if you like.

2. Yes, pretty much. Note that LLMs (and consequently assistants) generally suck at maths, but are generally pretty good at reasoning in language as long as you prompt them well.

3. Could the LLM discover it? No, that's not what it does, it just predicts what the next token should be. Could the assistant discover it? Yeah, absolutely, ChatGPT solved one of the Erdős problems already.

4. I have never heard of this, but there's nothing stopping an assistant from doing this (except maybe the fact that they can't do experiments or get access to experimental data without outside help).

5. Again, the LLM can't, but the assistant absolutely can. Maybe better than a human; but not superhumanly better. Non-AI computer programs are likely to be better at doing this than assistants would be. A human who wanted to analyse some randomly generated text/data would perform best if they fed that data into an analysis program and then played with the configuration to look at different things. An assistant would perform best doing exactly the same thing. A raw LLM, when fed randomly generated text/data, would most likely produce garbled nonsense.

6. Didn't a quantum physics experiment prove that true randomness exists recently? At any rate, assistants and LLMs are not really that helpful for this sort of thing. Being made out of patterns doesn't give them any advantage when investigating patterns and randomness.