[yannyu]

I never claimed we already know everything about LLMs. Knowing "everything about" anything these days is impossible given the complexity of our technology. Even antennae, a centuries old technology, is something we're still innovating on and don't completely understand in all domains.

But that's a categorically different statement than "no one understands how an LLM works", because we absolutely do.

You're spending a lot of time describing whether we know or don't know LLMs, but you're not talking at all about what it is that you think we do or do not understand. Instead of describing what you think the state of the knowledge is about LLMs, can you talk about what it is that you think that is unknown or not understood?

[LeroyRaz]

I think the person you are responding to is using a strange definition of "know."

I think they mean "do we understand how they process information to produce their outputs" (i.e., do we have an analytical description of the function they are trying to approximate).

You and I mean, we understand the training process that produces their behaviour (and this training process is mainly standard statistical modelling / ML).

In short, both sides are talking past each other.

[yannyu]

I agree. The two of us are talking past each other, and I wonder if it's because there's a certain strain of thought around LLMs that believes that epistemological questions and technology that we don't fully understand are somehow unique to computer science problems.

Questions about the nature of knowledge (epistemology and other philosophical/cognitive studies) in humans are still unsolved to this day, and frankly may never be fully understood. I'm not saying this makes LLM automatically similar to human intelligence, but there are plenty of behaviors, instincts, and knowledge across many kinds of objects that we don't fully understand the origin of. LLMs aren't qualitatively different in this way.

There are many technologies that we used that we didn't fully understand at the time, even iterating and improving on those designs without having a strong theory behind them. Only later did we develop the theoretical frameworks that explain how those things work. Much like we're now researching the underpinnings of how LLMs work to develop more robust theories around them.

I'm genuinely trying to engage in a conversation and understand where this person is coming from and what they think is so unique about this moment and this technology. I understand the technological feat and I think it's a huge step forward, but I don't understand the mysticism that has emerged around it.