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Comment by logicchains | Hacker News Reader

Theoretically transformers are fundamentally more powerful than stuff like Mamba and RWKV because they don't "forget"; the newest token can attend perfectly to the oldest token if it wants. Mamba and RWKV on the other hand compress the old state, based on information available at the time of compression. If it turns out the new token wants some information from the old token that during the compression was excluded due to the compression thinking it wouldn't be needed, the new token has no way to access it.

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This is fair -- the newest token can attend perfectly to the oldest token, within the context window.

but also, on a broader scale, if a transformer model is presented with a long input that does not fit in its context (e.g.: you are building a chatbot, and you have a very long chat history), it must "compress" or "forget" some of that information (e.g.: repeatedly summarizing historical messages, dropping them and appending the summary at the beginning of the input).

Mamba/RWKV/other "recurrent" architectures, can theoretically operate on unbounded input lengths; they "forget" information from earlier tokens over time, but is that not comparable to what a transformer must do with input lengths greater than their context window?

I didn’t know those alternatives had those weaknesses. I did have a whole list of alternatives to check out. Having already looked at some, do you know any transformer alternatives that don’t forget things but were still sub-quadratic? What rabbit holes are worth digging deeper into?

Doesn't sub-quadratic attention fundamentally imply forgetting things?

The jury very much still seems out on this. Computationally speaking, I believe Mamba is Turing complete, while transformers aren't (they can't do loops), so technically Mamba is more expressive. But of course, the question is always whether it ends up with lower loss.

(well actually its not turing complete, but it definitely seems closer)

Yet this all-to-all approach of transformers is bottlenecked on compute, while Mamba can take in a context length of a million tokens, that might have a positive effect on long range tasks.

What use is to have infinite context if new tokens can only access a lossy compression of past tokens?

There is definitely a use for this kind of model, but this also shows why Transformers are still the main architecture we use today.

I know almost nothing about this space but “lossy compression of millions of past tokens” feels a lot more like how actual human memory works than “perfect access to a small number of recent tokens”.

RNNs and LSTMs from the past did this as well (but cannot be trained in parallel as each token has to be compressed sequentially). Transformers ate their cake.

Newer methods are going back to similar concepts but trying to get past previous bottlenecks given what we've learned since then about transformers.

> feels a lot more like how actual human memory works

Beware of feel-good traps like this.

If you were to map the human connectome to a computational neural network down to the ion channel, it'd be at least 500 quadrillion parameters*. That's at least 5-6 orders of magnitude beyond what is currently possible with SOTA ML which means that even if the human brain was 99% devoted to compressing those tokens, that 1% that could actually do work with them is still a thousand times bigger than GPT4. There be emergent dragons.

* This is a fascile argument to begin with since biological neuron signals aren't quantized and ion channels are far too complex to map to a single static parameter

> biological neuron signals aren't quantized

Perhaps, but they inherently have imprecision due the nature of being biological. Expose the same neuron to the same input N times, and you'll get a range of output. The effect of noisy analog data is, broadly speaking, similar to the effect of low-resolution digital data.

Expose the same neuron to the same input N times and you won't get a randomly distributed range of outputs, you'll get a signal that is either attenuated or potentiated. That's not noise, that's the simplest form of neuroplasticity at work (which is why static parameters can never represent ion channels).

Maybe against HN rules, but: thank you. Very helpful comment!

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