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PaperclipPerfector  11mo ago (text post)   26074 thread views

Culture War Roundup for the week of December 4, 2023

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Interesting/mildly concerning.

I think it's a nothingburger because a) the future is cDPO/IPO and not orthodox RLHF anyway (or even more obscure things) and failure modes there will probably be different and b) such «misalignment» results in a behaviorally incoherent model rather than an evil schemer. Reward models are getting hacked by being dragged off-policy, with some weird inputs that are not conductive to strategic world understanding, it's an exploitation of the semiotic nature of language models. But I believe some hay will be made out of it.

Human «context size» is not at all limited to working memory (although our working memory is also large, it's not 5-9 tokens/bits but more like 5-9 «pointers» that can be corresponded to arbitrarily complex cognitive circuits). What we use for context is probably most analogous to constructing on the fly and loading a LoRA in LLMs (or some in-context vector) plus adding embeddings and snippets to some RAG pipeline. It's a mess, but it's orthogonal to the shift from Transformers to SSMs that I expect now. Shane Legg talks of this too:

They don't do things like episodic memory. Humans have what we call episodic memory. We have a working memory, which are things that have happened quite recently, and then we have a cortical memory, things that are sort of being in our cortex, but there's also a system in between, which is episodic memory, which is the hippocampus. It is about learning specific things very, very rapidly. So if you remember some of the things I say to you tomorrow, that'll be your episodic memory hippocampus.
Our models don't really have that kind of thing and we don't really test for that kind of thing. We just sort of try to make the context windows, which is more like working memory, longer and longer to sort of compensate for this.

As for RWKV, I think the latest version is ≤RetNet (though it has good slopes, probably the best in their graph…). Gu&Dao are very explicit in pointing out that a) Mamba the first to even match a Llama-like Transformer without any gimmicks, at the tested scale at least, and b) it does not appreciably benefit from adding Attention layers.

Mamba is the first attention-free model to match the performance of a very strong Transformer recipe (Transformer++) that has now become standard, particularly as the sequence length grows. We note that full results on context length 8k are missing for the RWKV and RetNet baselines, prior strong recurrent models that can also be interpreted as SSMs, due to a lack of efficient implementation leading to out-of-memory or unrealistic computation requirements.

The Mamba-MHA architecture is only slightly better, which is somewhat surprising in light of the fact that many recent works have found that combining (LTI) SSMs with Attention can lead to substantial improvements (Dao, Fu, Saab, et al. 2023; Fathi et al. 2023; Fathullah et al. 2023; Saon, Gupta, and Cui 2023; Zuo et al. 2022).

In the first version of the paper, submitted for peer review, they went even harder:

LongNet (Ding et al., 2023), which claimed to scale to 1B length but only evaluated on length < 100K for actual tasks. Hyena and HyenaDNA (Polietal.,2023;Nguyenetal.,2023),which claimed to leverage up to 1M context, but did not control for computation time. In fact, its claims about efficiency and performance would be largely matched by any of the LTI S4 variants above.

That said, this is all assuming the paper is trustworthy and they compare models trained on identical data. Tri obviously can procure as much compute as needed but I am not sure this happened.

but there's also a system in between, which is episodic memory, which is the hippocampus. It is about learning specific things very, very rapidly. So if you remember some of the things I say to you tomorrow, that'll be your episodic memory hippocampus.

It seems to me that LLMs can't have episodic memory, at least not till they're performing online learning, which nobody is carrying out as far as I'm aware.

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