I don't know much about language models, but don't they just have an understanding/knowledge of patterns between words, and don't have the reasoning capability at all?
I'm one of those people, minus the straw man of it "understanding" anything. Why do you assume a language model can't learn some rudimentary reasoning from predicting the next token?
That's because it's just a language model. It's been trained to find a probable completion to a piece of text, predict a likely next word. It's not trained for human interaction. It's not an agent. It has no motives or goals. It might seem like it does, but that's more of a side-effect of language modelling.
I don't particularly have any reason to believe one way or the other. Certainly, the probabilistic models for language are created "out of the blue" without any attempt to model how human learn languages.
Given that large language models don't have any actual knowledge (in a human sense) of the data they've been trained on other than raw statistics, can they be said to "reason" about anything?
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