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Scaling Transformer to 1M tokens and beyond with RMT

arxiv.org

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Re: Scaling Transformer to 1M tokens and beyond with RMT

#51

Post by macleginn »

zwaps wrote: post #28

I really wish that CS would switch to discussing related literature before (or with) contributions. The authors only cite themselves until the very end of the paper, and the whole time you are thinking: "Hasn't this been done before? Transformer-XL (recurrent Memory) is a years old paper!" And you need to read to the very and to find out that the contribution is actually: Keep your architecture (but still need to tra…

They cite the paper where the architecture was introduced. If you go to that paper, you'll see that it mostly consists of a very detailed and careful comparison with Transformer-XL.

In the new paper, they plug their memory system into vanilla BERT. This makes the resulting model essentially nothing like Transformer-XL, which was a strictly decoder-only generative language model.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#52

Post by bckr »

baobabKoodaa wrote: post #49
nl wrote: post #41

Earlier quoted context omitted.

> The “learning” in “In context learning” is not the same concept as learning during the network “training”. The former is loosely named and does not have any impact on what’s actually “learned” by the model. Actually (and very surprisingly!) it is related. See the "Similarity of Attention Output Updates" and "Similarity of Attention Map" metrics in https://arxiv.org/abs/2212.10559 (linked above) where they show that…

Whoa. How is that possible?

Yeah, what is being stored that is available between inferences?

Re: Scaling Transformer to 1M tokens and beyond with RMT

#53

Post by willbudd »

nl wrote: post #31
kgwgk wrote: post #29

Earlier quoted context omitted.

> (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this) Do you? Some of them -at least- understand that the output is conditional on the input.

"Conditioned" with the ability to introduce new facts and have the model infer impacts on them. As an example, tell chatGPT that the Queen of England died (which occurred after the data cut off) and then ask it who the head of state of Australia is. It's able to infer the head of state of Australia is now Charles III (and gives a good explanation of how this is mostly ceremonial.) See https://twitter.com/nlothian/sta…

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 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.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#54

Post by [deleted] »

alchemist1e9 wrote: post #12
tomohelix wrote: post #7

An AI can theoretically ingest 1M token of data, analyze, summarize, indexing, and storing the condensed information in a hard drive. Then later on retrieve it based on the metadata for specific situations that needs it. At millions of token, it is basically a superhuman that can learn new things on the fly at the speed unimaginable to any organic based life. We are so close to something amazing, and scary.

Do we yet have a single example of a transformer based AI, i.e. LLM, learning something new that we didn’t teach it in the training data? Maybe we do. I’m not sure how to define it, but new should be a discovery or insight or even relationship that is not explicitly taught in the training data. If we don’t then it suggests without humans in the loop that the super intelligence is not so close?

[deleted]

Re: Scaling Transformer to 1M tokens and beyond with RMT

#55

Post by kmac_ »

geraneum wrote: post #46
nl wrote: post #31

Earlier quoted context omitted.

"Conditioned" with the ability to introduce new facts and have the model infer impacts on them. As an example, tell chatGPT that the Queen of England died (which occurred after the data cut off) and then ask it who the head of state of Australia is. It's able to infer the head of state of Australia is now Charles III (and gives a good explanation of how this is mostly ceremonial.) See https://twitter.com/nlothian/sta…

Interestingly, the example provided is not necessarily indicative of the ability to infer the actual physical transfer of power. It’s possible that the terms “After Elisabeth, Charles will be king” are accompanied with “Elisabeth” and “Die” or “Death” in the same context before cut off (Wikipedia, news papers, tabloids). You cannot draw any scientifically sound conclusions without knowing what was actually in the tra…

Yeah, I tested that, but asked what happens if both Charles and William abdicate - got this result: "However, if both Charles III and his son abdicate, it is not clear who would become the head of state in Australia. There is a possibility that Australia could become a republic and have its first Australian head of state." Clearly, it didn't handle quite straightforward inferring (Prince George of Wales, Williams son).

Re: Scaling Transformer to 1M tokens and beyond with RMT

#56

Post by willbudd »

geraneum wrote: post #46
nl wrote: post #31

Earlier quoted context omitted.

"Conditioned" with the ability to introduce new facts and have the model infer impacts on them. As an example, tell chatGPT that the Queen of England died (which occurred after the data cut off) and then ask it who the head of state of Australia is. It's able to infer the head of state of Australia is now Charles III (and gives a good explanation of how this is mostly ceremonial.) See https://twitter.com/nlothian/sta…

Interestingly, the example provided is not necessarily indicative of the ability to infer the actual physical transfer of power. It’s possible that the terms “After Elisabeth, Charles will be king” are accompanied with “Elisabeth” and “Die” or “Death” in the same context before cut off (Wikipedia, news papers, tabloids). You cannot draw any scientifically sound conclusions without knowing what was actually in the tra…

Au contraire. Learning an abstract logical relationship such as line of succession during training, and then applying substitution/reification during inference to deduce the new factual clause that Charles is king of the UK is exactly what it means to learn something new. It's just a pity it can't memorize this fact at inference time, and that won't be able to reproduce it as soon as the information about the queen's death slides outside of the context window.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#57

Post by int_19h »

nl wrote: post #35
macrolime wrote: post #30

Earlier quoted context omitted.

GPT-4 has toolformer capabilities as emergent properties, so using it in combination with toolformer is really unnecessary.

This is true too. Even chatGPT can learn some tool use in-context. See https://twitter.com/minosvasilias/status/1627076214639976449

GPT-4 writes some wicked complicated (but correct!) SQL if given a schema and a relevant task:

https://gist.github.com/int19h/428cea1d87dfc389b99de1b79727f...

What I found really amazing about this particular experiment is that the schema I gave it didn't contain any information that could be used to query for things like distances between places, and yet it came up with the idea of using settlements' culture as a proxy to determine "border fiefs", completely unprompted (and yes, it actually is a very effective proxy for this particular case!).

I wonder now what it would do with Prolog - or maybe Datalog for simplicity? - although that might depend on how much of it there is in the training data. Do you know if anyone tried it yet?

Re: Scaling Transformer to 1M tokens and beyond with RMT

#58

Post by willbudd »

kmac_ wrote: post #55
geraneum wrote: post #46

Earlier quoted context omitted.

Interestingly, the example provided is not necessarily indicative of the ability to infer the actual physical transfer of power. It’s possible that the terms “After Elisabeth, Charles will be king” are accompanied with “Elisabeth” and “Die” or “Death” in the same context before cut off (Wikipedia, news papers, tabloids). You cannot draw any scientifically sound conclusions without knowing what was actually in the tra…

Yeah, I tested that, but asked what happens if both Charles and William abdicate - got this result: "However, if both Charles III and his son abdicate, it is not clear who would become the head of state in Australia. There is a possibility that Australia could become a republic and have its first Australian head of state." Clearly, it didn't handle quite straightforward inferring (Prince George of Wales, Williams son…

Or maybe it did. Who knows. If "both Charles III and his son abdicate" could well be considered indicative of some large upheaval or scandal, at which point it is entirely conceivable that the Australian electorate reaches a consensus on becoming a republic. The way that is phrased doesn't seem like a straightforward proposition to me at all.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#59

Post by mike_hearn »

bckr wrote: post #52
baobabKoodaa wrote: post #49

Earlier quoted context omitted.

Whoa. How is that possible?

Yeah, what is being stored that is available between inferences?

Nothing, if by "inference" you mean single API call. The underlying algorithm starts (very roughly) by processing the prompt into a set of vectors, which are then used repeatedly to generate tokens one after the other. What's meant here is that the act of generating and using those prompt vectors has an effect sort of like regular training. It doesn't mean anything is being written to disk afterwards, though of course you could write those vectors to disk and re-use them. It just doesn't make much sense at the moment. Doing that isn't enough to give you infinite context windows.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#60

Post by mike_hearn »

kmac_ wrote: post #55
geraneum wrote: post #46

Earlier quoted context omitted.

Interestingly, the example provided is not necessarily indicative of the ability to infer the actual physical transfer of power. It’s possible that the terms “After Elisabeth, Charles will be king” are accompanied with “Elisabeth” and “Die” or “Death” in the same context before cut off (Wikipedia, news papers, tabloids). You cannot draw any scientifically sound conclusions without knowing what was actually in the tra…

Yeah, I tested that, but asked what happens if both Charles and William abdicate - got this result: "However, if both Charles III and his son abdicate, it is not clear who would become the head of state in Australia. There is a possibility that Australia could become a republic and have its first Australian head of state." Clearly, it didn't handle quite straightforward inferring (Prince George of Wales, Williams son…

Did you tell it that it was being given a logic test? Unfortunately a lot of logic questions can lead to outcomes like this, where the "obvious" or "real" answer is not the one implied by strict logical reasoning. Then when people give answers useful in the real world, they are marked as having failed.
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