> LLMs process information in a strictly sequential manner.
"LLMs" as a class do not. Most LLMs, because most LLMs are autoregressive models, but diffusion LLMs exist and are not sequential in the way that autoregressive models are.
> It's their core capability
Being sequential is not a capability at all, much less a core one defining Large Language Models.
> and what makes them feel so anthropomorphic.
I disagree with this, too; I think what makes LLMs "feel so anthropomorphic" is the fact that most humans are very focused on language in perceiving other humans as human, and LLMs' output (as their name suggests) models human use of language, directly targeting a key feature used to identify something as human-like.
The gimmick of the LLM is that it outputs text sequentially, as if it is talking to us. That's what makes them feel "alive" and "intelligent" to us. (And yes, ironically it's this sequential nature that actually limits their intelligence in practice, but whatever. The AI hype is about appearances, not facts.)
> That's what makes them feel "alive" and "intelligent" to us.
What is the basis for this claim? Seems like "A" (chatbots output text sequentially) is true, and "B" (they feel intelligent to us) is true, and you're claiming "A causes B" without any support at all. Just because they happen to both be true and you personally feel there is a causal relationship, which proves nothing.
> The gimmick of the LLM is that it outputs text sequentially, as if it is talking to us. That's what makes them feel "alive" and "intelligent" to us.
Yes, I got that that was the original claim. I still disagree with us. What makes them feel alive and intelligent is that they produce human-like language output, not that the process by which they construct that output is sequential. Non-autoregressive LLMs of equal output quality would (do) appear just as alive and intelligent as autoregressive LLMs. An autoregressive LLM behind a non-streaming request/response interface where the token-by-token sequencing of the response is not exposed to the user still seems just as intelligent as one where the output is streamed to the user.
Yes. Human speech is sequential (we make sounds one by one), and when LLMs mimic this with token-by-token autocomplete they seem more anthropomorphic to us.
(I take issue with the word "successful", though. Selling LLMs as a human-like intelligence is a gimmick and a borderline scam.)
The point of transformer attention is cross-wise processing of tokens that computes their relationship to each other at multiple levels of abstraction. That's why LLMs can read so fast: they're processing all the input tokens in parallel.
LLMs emit tokens in a sequential manner at the level of the outer loop, but clearly inside the activations is a non-sequential map of the entire planned output, otherwise they wouldn't be able to make coherent sentences or speak German (which puts verbs at the end).
