Scaling Transformer to 1M tokens and beyond with RMT

Re:Scaling Transformer to 1M tokens and beyond with RMT by willbudd on Monday April 24, 2023 @03:39AM (#35684723)

That's a brilliant example. Thanks for sharing. It demonstrates in a very straightforward way that LLMs are capable of learning (and applying) relationships at the level of abstraction of (at least) 1st order logic.

It implies that during training, it learned the facts that Elizabeth is queen of the UK, and that Charles is its crown prince; but _also_ the logical rule <IF die(monarch) AND alive(heir_to_the_throne) => transform(heir_to_the_throne, monarch) AND transform(monarch, former_monarch)>, or at least something along those lines that allows similarly powerful entailment. And that in addition to the ability to substitute/reify with the input sequence at inference runtime.

Would be nice to see a rigorous survey of its logical capabilities given some complex Prolog/Datalog/etc knowledge-base as baseline.

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Re:Scaling Transformer to 1M tokens and beyond with RMT by wokkel on Monday April 24, 2023 @07:31AM (#35686282)

No it does not: if you google this and restrict the time to before 2021 (the learning cutoff date) you will find the same answer. Without having access to the training data it's impossible to tell what we seeing.

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Re:Scaling Transformer to 1M tokens and beyond with RMT by willbudd on Monday April 24, 2023 @07:49AM (#35686421)

> No it does not: if you google this and restrict the time to before 2021 (the learning cutoff date) you will find the same answer.

Not entirely sure what you mean, but ...show me? Why not just share a link instead of making empty assertions?

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Re:Scaling Transformer to 1M tokens and beyond with RMT by nobody81147 on Monday April 24, 2023 @08:27AM (#35686789)

Here’s a Quora thread from 4 years ago:

https://www.quora.com/Once-Queen-Elizabeth-dies-will-Prince-...

There are loads of articles and discussions online speculating about what “will” happen when Queen Elizabeth dies.

When you have a very, very, very large corpus to sample from, it can look a lot like reasoning.

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Re:Scaling Transformer to 1M tokens and beyond with RMT by willbudd on Monday April 24, 2023 @09:47AM (#35687591)

I see what you mean, and it's indeed quite likely that texts containing such hypothetical scenarios were included in the dataset. Nonetheless, the implication is that the model was able to extract the conditional represented, recognize when that condition was in fact met (or at least asserted: "The queen died."), and then apply the entailed truth. To me that demonstrates reasoning capabilities, even if for example it memorized/encoded entire Quora threads in its weights (which seems unlikely). If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck.

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Re:Scaling Transformer to 1M tokens and beyond with RMT by nl on Monday April 24, 2023 @09:10PM (#35695365)

Yes, this.

There's clearly an internal representation of the relationships that is being updated.

If you follow my Twitter thread it shows some temporal reasoning capabilities too. Hard to argue that is just copied from training data: https://twitter.com/nlothian/status/1646699218290225154

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Re:Scaling Transformer to 1M tokens and beyond with RMT by nl on Monday April 24, 2023 @09:07PM (#35695344)

That's not the same thing at all.

It absolutely needed to know who the successor would be via training data.

But to know that "The Queen of England died" also means that the head of state of Australia has changed means that it has an internal representation of those relationships.

(Another way of seeing this is with multi-modal models where the visual concepts and word concepts are related enough it can map between the two.)

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