[qsera]

>Modelling text describing the world is not modelling (some aspect) of the world?

The text describes the world to humans. This is the crucial thing that you miss. It is very subjective.

Imagine that you learn the grammar of a foreign language without learning the meaning of the words. You might be able to make grammatically valid sentences. But you will still will not understand a single thing that something written in that language describes. But that will be perfectly clear to someone who actually understand the meaning of the words.

When you train LLMs on large volumes of text that describe logically consistent facts in a million different ways, the "logic" sort of becomes part of the grammer that the model learns. That is logic becomes a higher kind of "grammer" or a enormous set of grammatical rules that it captures. But that does not mean the model can do actual logic.

[NooneAtAll3]

> Imagine that you learn the grammar of a foreign language without learning the meaning of the words. You might be able to make grammatically valid sentences. But you will still will not understand a single thing that something written in that language describes. But that will be perfectly clear to someone who actually understand the meaning of the words.

so... back to chinese room arguments?

just because amazon worker inside is just moving folders around following rules, doesn't by default mean the room as a whole can't be corresponding to "something that doesn't understand"

denying emergence as a phenomenon isn't useful when "there are plenty of higher abstraction levels in multiple fields that still capture 99% of events and are easier to model and react to" is the counterpoint

[libraryofbabel]

> When you train LLMs on large volumes of text that describe logically consistent facts in a million different ways, the "logic" sort of becomes part of the grammer that the model learns. That is logic becomes a higher kind of "grammer" or a enormous set of grammatical rules that it captures. But that does not mean the model can do actual logic.

This is the kind of stuff people were saying in 2023. But it’s 2026 now and LLMs aren’t just trained by reading lots of text anymore. That’s “pretraining”, and it’s still the first stage, but LLMs also have a huge amount of RLVR training where they actually do solve huge numbers of mathematical and logic puzzles and update their weights in response. They don’t just learn mathematics from reading about it now. They learn it by doing it. That is why they can now solve hard problems and probe theorems.

> that does not mean the model can do actual logic.

But they do, all the time. (Please tell me you’ve at least put a frontier LLM through its paces in the last 6 months?) If you think they can’t do logic and reasoning, can you provide examples of specific math or logic problems that you think a frontier LLM can’t do?

[qsera]

>If you think they can’t do logic and reasoning, can you provide examples of specific math or logic problems that you think a frontier LLM can’t do?

When a thing can "solve" a complex math problem without having the ability to count, then it is clear that this things is not "reasoning" and doing "logic".

[libraryofbabel]

You didn’t answer my question. You just restated your claims.

Specific examples? Specific tasks?

[hellohello2]

Thanks for your explanation, I find it much more intuitive than the paper's.

In your opinion, does a Calculus solver model certain aspects of the world?