A non-anthropomorphized view of LLMs

I have the technical knowledge to know how LLMs work, but I still find it pointless to not anthropomorphize, at least to an extent.

The language of "generator that stochastically produces the next word" is just not very useful when you're talking about, e.g., an LLM that is answering complex world modeling questions or generating a creative story. It's at the wrong level of abstraction, just as if you were discussing an UI events API and you were talking about zeros and ones, or voltages in transistors. Technically fine but totally useless to reach any conclusion about the high-level system.

We need a higher abstraction level to talk about higher level phenomena in LLMs as well, and the problem is that we have no idea what happens internally at those higher abstraction levels. So, considering that LLMs somehow imitate humans (at least in terms of output), anthropomorphization is the best abstraction we have, hence people naturally resort to it when discussing what LLMs can do.

> We need a higher abstraction level to talk about higher level phenomena in LLMs as well, and the problem is that we have no idea what happens internally at those higher abstraction levels

We do know what happens at higher abstraction levels; the design of efficient networks, and the steady beat of SOTA improvements all depend on understanding how LLMs work internally: choice of network dimensions, feature extraction, attention, attention heads, caching, the peculiarities of high-dimensions and avoiding overfitting are all well-understood by practitioners. Anthropomorphization is only necessary in pop-science articles that use a limited vocabulary.

IMO, there is very little mystery, but lots of deliberate mysticism, especially about future LLMs - the usual hype-cycle extrapolation.