Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Scaling Transformer to 1M tokens and beyond with RMT

arxiv.org

Post reply on HN
Print view View original

41–50 of 147 posts

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

#41

Post by nl »

geraneum wrote: post #37
nl wrote: post #26

For those who don't completely get the impact of this: We already know Large Language Models (LLMs) can learn at runtime (ie, separately to the training process.) This is called "In Context Learning". See [1], [2] for more details. (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this) In context learning is wonderful because it means you can "train" a LLM at run time by filling the…

> In context learning is wonderful because it means you can "train" a LLM at run time 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. The LLM can, by nature, contradict its training data and whatever you/it prompt(s), and incorporates randomness when completing…

> 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 in-context learning actually changes the attention map in a similar way to traditional fine tuning(!!).

Obviously it can't modify the weights of the model on disk, but after ICL new passes through the model are treated quite similarly to how they would be if it has been fine tuned (again: this is astonishing!)

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

#43

Post by [deleted] »

albertzeyer wrote: post #34

This looks like a potential NeurIPS submission. But it will probably be rejected. The quality bar for NeurIPS is quite high. Some reasons: The experiments are very weak: There are just a few figures, basically figure 1 and figure 5, which show some results. There are no tables with numbers. But more importantly: There are no comparisons (in terms of experiments/numbers) to similar models, like: - Block-Recurrent Tran…

[deleted]

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

#44

Post by mejutoco »

nl wrote: post #22
alchemist1e9 wrote: post #12

Earlier quoted context omitted.

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?

There are plenty of examples of "new" things a LLM can do. A good example is all those toy examples of "Program a whatever in the style of Shakespeare and David Bowie's love child". This isn't a thing that it has seen in training data.

Not saying it is not fascinating but, isn't this program something 50% in the style of Shakespeare and 50% in the style of David Bowie?

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

#45

Post by brucethemoose2 »

albertzeyer wrote: post #34

This looks like a potential NeurIPS submission. But it will probably be rejected. The quality bar for NeurIPS is quite high. Some reasons: The experiments are very weak: There are just a few figures, basically figure 1 and figure 5, which show some results. There are no tables with numbers. But more importantly: There are no comparisons (in terms of experiments/numbers) to similar models, like: - Block-Recurrent Tran…

I feel like this is a thing in ML land, like everyone is in such a rush to publish something "revolutionary" because the whole field is already moving at warp speed. Under such pressure, its easy to lose focus on metrics and comparisons.

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

#46

Post by geraneum »

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…

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 training data and use a control group/instance.

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

#47

Post by macleginn »

albertzeyer wrote: post #34

This looks like a potential NeurIPS submission. But it will probably be rejected. The quality bar for NeurIPS is quite high. Some reasons: The experiments are very weak: There are just a few figures, basically figure 1 and figure 5, which show some results. There are no tables with numbers. But more importantly: There are no comparisons (in terms of experiments/numbers) to similar models, like: - Block-Recurrent Tran…

The original architecture used in this model was accepted at last year's NeurIPS: https://proceedings.neurips.cc/paper_files/paper/2022/file/4...

That paper is written very differently.

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

#48

Post by int_19h »

nl wrote: post #40
kgwgk wrote: post #36

Earlier quoted context omitted.

According to the parent comment anyone who says "LLMs are stochastic parrots" is ignorant of the effect that changing the input “filling the context with examples” has on the output.

That isn't a fair summary of what I'm saying. People who say "LLMs are stochastic parrots" may well be aware of effects of conditioning the input. This in itself does not completely describe the capabilities of a LLM, since their ability to learn and use those new facts to override their previous "beliefs" is not what you'd expect from mere stochastic behavior. My point is that "stochastic parrot" believers are unawa…

They are aware. The thing about the "stochastic parrot" argument is that it can be pushed as far as one is willing to push it; any behavior by an LLM can be described in those terms with enough handwaving.

The practical limit seems to be where one would have to say that humans are also "stochastic parrots", presumably because that defeats the point of making such an argument in the first place.

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

#49

Post by baobabKoodaa »

nl wrote: post #41
geraneum wrote: post #37

Earlier quoted context omitted.

> In context learning is wonderful because it means you can "train" a LLM at run time 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. The LLM can, by nature, contradict its training data and whatever you/it prompt(s), and incorporates randomness when completing…

> 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?
Post reply on HN