[lo_zamoyski]

Conversely, human beings with varying particular experiences can come to the same conclusions, because human cognition can abstract from particulars, while LLMs are, at best, statistical and imagist. No two of us ever experience the same set of triangles, but abstraction allows us to form concepts like "triangularity", which means we can understand what it means to be a triangle per se (something that is not concrete or particular, and therefore cannot be visualized), while an LLM can only proceed based on the concrete and particular data of input triangles and derivations introduced into the model. It can never go "beyond" the surface features of the training model's images, as it were, and where the appearance of having done so occurs, it is not via abstraction, but by way of product of human abstraction. From the LLM's perspective, there is no triangle, only co-occurrence of features, while abstraction goes beyond features, stripping them away to obtain the bare, unimaginable form.

[the_gipsy]

Different LLMs can also come to the same conclusions, that is, predict the same strings of tokens (in the meaning).

Sure, they're a far way from the capacity of humans in terms of short term training. But there is literally nothing that indicates they can't "think" (understand, reason, abstract, whatever word you wanna put in italics) because nobody can explain what it really means, because: it's all just predicting. Text happens to be super useful for us to evaluate certain aspects of predicting.