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Scaling Transformer to 1M tokens and beyond with RMT
61–70 of 147 posts
Re: Scaling Transformer to 1M tokens and beyond with RMT
#62[flagged]
Re: Scaling Transformer to 1M tokens and beyond with RMT
#63This paper presents a novel approach to incorporate memory into a transformer. It does not demonstrate that this approach works in a useful manner. While the approach is interesting, I’m skeptical that the RNN has enough capacity to encode the memory in it's output. I would have liked to see more detail on the synthetic benchmark they used. The memory component may be learning the benchmark rather than a generalized…
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Re: Scaling Transformer to 1M tokens and beyond with RMT
#64Abstract This technical report presents the application of a recurrent memory to extend the context length of BERT, one of the most effective Transformer-based models in natural language processing. By leveraging the Recurrent Memory Transformer architecture, we have successfully increased the model's effective context length to an unprecedented two million tokens, while maintaining high memory retrieval accuracy. Ou…
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Re: Scaling Transformer to 1M tokens and beyond with RMT
#65Earlier 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…
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…
I'm curious if future developments in LLMs will enable them to extract significant/noteworthy info from their context window and incorporate it into their underlying understanding by adjusting their weights accordingly. This could be an important step towards achieving AGI, since it closely mirrors how humans learn imo.
Humans continually update their foundational understanding by assimilating vital information from their "context window" and dumping irrelevant noise. If LLMs could emulate this, it would be a huge win.
Overall, very exciting area of research!
Re: Scaling Transformer to 1M tokens and beyond with RMT
#66This 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…
A model of publishing in which the authors of related work are compensated (citations, appearing as coauthors,...) would allow new approaches and ideas to be disseminated easily. The main factor here is about novelty and possible applications of the new approaches.
Re: Scaling Transformer to 1M tokens and beyond with RMT
#67Earlier 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?
An hypothesis is that in the latent space there is something like a branching factor that can be used in context learning to select the main tree for others layers. So a LLM is able to have the knowledge of many specialized smallers LLM and select the appropriate one by way of giving values to the branching factor. The branching factor could be some combination of attention layers operating in the latent space of previous layers.
Re: Scaling Transformer to 1M tokens and beyond with RMT
#68For 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…
> GPT-4 recently extended that to 32,000 tokens
Slightly unrelated, but has anyone outside of OpenAI got access to this model yet? While I received API access to GPT4 just a day or two after applying for it, I've yet to succeed to get access to the 32k version, nor has anyone I know, and I have not seen it being used by anyone in the wild either.
Re: Scaling Transformer to 1M tokens and beyond with RMT
#69Earlier 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…
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…
That’s actually correct but an overfitted definition for learning. It holds certain hidden assumptions (i.e physical grounding) of the learner being human which makes it inapplicable to an LLM. As in a self driving car which passes a driving exam but fails to drive effectively freely in the city (it’s not an LLM but relevant in this context). You have to admit when you work with this tech that something fundamental is missing in how they perform.
Re: Scaling Transformer to 1M tokens and beyond with RMT
#70Earlier quoted context omitted.
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.
I verified that it has all required facts (line of succession, current circumstances). I managed to get the right answer when got everything in context, but it failed again when all three abdicate (same context). Prince Harry was indicated once.
I tested GPT a lot in other domains, what I found that as long the information explicitly exists (connection between facts) then the responses are fine. I assume that if GPT will reach the state where it can infer new facts, we will be flooded with discoveries that require cross domain knowledge. Nothing like that happened yet.