From multi-head to latent attention: The evolution of attention mechanisms | Hacker News Reader

From multi-head to latent attention: The evolution of attention mechanisms

vinithavn.medium.com

174 pointsmgninad41 comments

"Attention Is All You Need" - I've always wondered if the authors of that paper used such a casual and catchy title because they knew it would be groundbreaking and massively cited in the future....

Definitely. I always assumed that, having been involved in writing similarly groundbreaking papers… or so we thought at the time. All my coauthors spent significant time thinking about what the best title would be, and strategies like that were common. (It ended up not mattering for us.)

The transformer was a major breakthrough in NLP, and it was clear at the time of publishing that it would have a major impact. But I will add that it is common in the Deep Learning field to give papers catchy titles (see, off the top of my head: all the YOLO papers, ViT, DiT, textual inversion). The transformer paper is one in a long line of seminal papers with funny names.

Attention is all you need for what we have. But attention is a local heuristic. We have brittle coherence and no global state. I believe we need a paradigm shift in architecture to move forward.

Has there been research into some hierarchical attention model that has local attention at the scale of sentences and paragraphs that feeds embeddings up to longer range attention across documents?

There’s the hierarchical reasoning model https://arxiv.org/abs/2506.21734 but it’s very new and largely untested

Though honestly I don’t think new neural network architectures are going to get us over this local maximum, I think the next steps forward involve something that’s

1. Non lossy

2. Readily interpretable

The ARC Prize Foundation ran extensive ablations on HRM for their slew of reasoning tasks and noted that the "hierarchical" part of their architecture is not much more impactful than a vanilla transformer of the same size with no extra hyperparameter tuning:

https://arcprize.org/blog/hrm-analysis#analyzing-hrms-contri...

By now, I seriously doubt any "readily interpretable" claims.

Nothing about human brain is "readily interpretable", and artificial neural networks - which, unlike brains, can be instrumented and experimented on easily - tend to resist interpretation nonetheless.

If there was an easy to reduce ML to "readily interpretable" representations, someone would have done so already. If there were architectures that perform similarly but are orders of magnitude more interpretable, they will be used, because interpretability is desirable. Instead, we get what we get.

From what I’ve seen neurology is very readily interpretable but it’s hard to get data to interpret. For example the visual cortex V1-V5 areas are very well mapped out but other “deeper” structures are hard to get to and meaningfully measure.

They're interpretable in a similar way to how interpretable CNNs are. Not by a coincidence.

For CNNs, we know very well how the early layers work - edge detectors, curve detectors, etc. This understanding decays further into the model. In the brain, V1/V2 are similarly well studied, but it breaks down deeper into the visual cortex - and the sheer architectural complexity there sure doesn't help.

Well, in terms of architectural complexity you have to wonder what something intelligent is going to look like, it’s probably not going to be very simple, but that doesn’t mean it can’t be readily interpreted. For the brain we can ascribe structure to evolutionary pressure, IMO there isn’t quite as powerful a principle at play with LLMs and transformer architectures and such. Like how does minimizing reconstruction loss help us understand the 50th, 60th layer of a neural network? It becomes very hard to interpret, compared to say the function of the amygdala or hippocampus in the context of evolutionary pressure.

Plenty of "we need a paradigm shift in architecture" going around - and no actual architecture that would beat transformers at their strengths as far as eye can see.

I remain highly skeptical. I doubt that transformers are the best architecture possible, but they set a high bar. And it sure seems like people who keep making the suggestion that "transformers aren't the future" aren't good enough to actually clear that bar.

That logic does not hold.

Being able to provide an immediate replacement is not a requirement to point out limitations in current technology.

What's the value of "pointing out limitations" if this completely fails to drive any improvements?

If any midwit can say "X is deeply flawed" but no one can put together an Y that would beat X, then clearly, pointing out the flaws was never the bottleneck at all.

I think you don't understand how primary research works. Pointing out flaws helps others think about those flaws.

It's not a linear process so I'm not sure the "bottleneck" analogy holds here.

We're not limited to only talking about "the bottleneck". I think the argument is more that we're very close to optimal results for the current approach/architecture, so getting superior outcomes from AI will actually require meaningfully different approaches.

Where's that "primary research" you're talking about? I certainly don't see it happening here right now.

My point is: saying "transformers are flawed" is dirt cheap. Coming up with anything less flawed isn't.

> What's the value of "pointing out limitations" if this completely fails to drive any improvements?

Ironically, the same could be said about Attention Is All You Need in 2017. It didn’t drive any improvements immediately- actual decent Transformer models took a few years to arrive after that.

what ever happened to Google's Titans?

To be fair it would be a lot easier to iterate on ideas if a single experiment didn't cost thousands of dollars and require such massive data. Things have really gotten to the point that it's just not easy for outsiders to contribute if you're not part of a big company or university, and even then you have to justify the expenditure (risk). Paradigm shifts are hard to come by when there is so much momentum in one direction and trying something different carries significant barriers.

Plenty of research involves small models trained on small amounts of data. You don't necessarily need to do an internet-scale training run to test a new model architecture, you can just compare it to other models of the same size trained on the same data. For example, small-model speedruns are a thing: https://github.com/KellerJordan/modded-nanogpt

The Transformer was only ever designed to be a better seq-2-seq architecture, so "all you need" implicitly means "all you need for seq-2-seq" (not all you need for AGI), and was anyways more backwards looking than forwards looking.

The preceding seq-2-seq architectures had been RNN (LSTM) based, then RNN + attention (Bahdanau et al "Jointly Learning to Align & Translate"), with the Transformer "attention is all you need" paper then meaning you can drop use of RNNs altogether and just use attention.

Of course NOT using RNNs was the key motivator behind the new Transformer architecture - not only did you not NEED an RNN, but they explicitly wanted to avoid it since the goal was to support parallel vs sequential processing for better performance on the available highly parallel hardware.

I recommend reading this article which explains how you can get your papers accepted, and explains that a catchy title is the #1 most important thing: https://maxwellforbes.com/posts/how-to-get-a-paper-accepted/ (not a plug, I just saved it because it was interesting)

It sounds like a typical neurips paper to me. And no, they did know what a big deal it would be, else google never would have given the idea away.

It is a reference to the beatles song, mainly because Noam Shazeer is a music lover

What about the converse, the paper became some massively influential because of the catchy title? Of course the contents are groundbreaking, but that alone is not enough. A groundbreaking paper that nobody knows about cannot have any impact. Even for research, there is a marketing part to it.

Huh? of course its enough. Transformers immediately started destroying every single baseline out there. The authors definitely knew it was a very significant discovery beforehand.

The paper became massively influential because of its contents, not its catchy title. Scientists do not generally read a paper because if its title, they check the abstract and go from there.

The title is a succinct snippet that spoke directly to researchers at the time. The transformer architecture is somewhat obvious (especially in retrospect), but it was still very surprising because no one was really going this direction. They were going many other directions… That’s the point of the title: you don’t need all kinds of complicated systems for NLP to work—“attention is all you need”.

After the success of transfer learning for computer vision in the mid-2010s, it was obvious that NLP needed its own transfer learning approach and AlexNet moment.

Lots of research focus around that time was on recurrent models—because that was the conventional wisdom about how you model sequences. Markov chains had led to vanilla RNNs, LSTMs, GRU, etc., which all seemed tantalizingly promising. (MAMBA fans take note.) Attention mechanisms were even used in recurrent models…but so was everything else.

Then came transformers—mixing all the then-best-practice bits with the heretical idea of just not giving a shit about O(n^2) complexity. The vanilla transformer used an encoder-decoder structure like the best translation models had been doing; it used a stack of identical blocks to nudge the output along through the pipe like ResNet; it was pretrained on a multi-task objective using a large document corpus. But then it jettisoned all the other complexity and just let it all rest on the attention mechanism to capture long range dependencies.

It was immediately thrilling, but it was also completely impractical. (I think the largest model had a 500ish token context limit and bigger than hobbyist GPUs.) So it mostly sat on a shelf while people used other “good enough” models for a few years until the hardware got better and a couple folks proved that it could actually work to run these things at massive scale.

And now here we are.

I think they knew what they were saying at the time, but I don’t think they knew that it would remain true for years.

I feel like there is a step missing here...

People were using RNN encoders/decoders for machine translation - the encoder was used to make a representation (fixed-size vector) of the source language sentence, the decoder generated the target language sentence from the source representation.

The issue that people were bumping into is that the fixed-sized vector bottlenecked the encoder/decoder architecture. Representing a variable-length source sentence as a fixed-size vector leads to a loss of information that increases with the source sentence length.

People started adding attention to the decoder as a way to work around this issue. Each decoder step could attend to every token (well, RNN hidden representation) of the source sentence. So, this led to the RNN + attention architecture.

The title 'Attention is all you need' comes from the realization that in this architecture the RNN is not needed, neither for the encoder and decoder. It's a message to the field who was using RNNs + attention (to avoid the bottleneck). Of course, the rest was born from that, encoder-only transformer models like BERT and decoder-only models like current LLMs.

This is a fair point and clarification. :-)

I was there. They had no idea. The purpose and expectation of the transformer architecture was to enable better translation at scale and without recurrence. This in itself would have been a big deal (and they probably expected some citations), but the actual impact of the architecture was a few orders of magnitude greater.

Do we know if any of these techniques are actually used in the so-called "frontier" models?

The model names are mentioned under each type of attention mechanism

Who knows what the closed source models use but certainly going by what's happening in open models all the big changes and corresponding gains in capability are in training techniques not model architecture. Things like GQA and MLA as discussed in this article are important techniques for getting better scaling but are relatively minor tweak vs the evolution in training techniques.

I suspect closed models aren't doing anything too radically different from what's presented here.

Not quite a frontier model but definitely built by a frontier lab: Grok 2 was recently open sourced and I believe it uses a fairly standard MHA architecture with MoE.

Any way to read this without making an account?

https://freedium.cfd/https://vinithavn.medium.com/from-multi...

https://freedium.cfd

Just click the x at the top right of the interstitial?

That only work for a few articles per month. But usually opening in incognito does the trick.

just turn off JS.

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