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This result shouldn't be underestimated because Gemini-Ultra is merely on par/slightly better in text-based reasoning: it thoroughly beats GPT-4V on MMMU, the multimodal benchmark, including harder subscales; it also plays well with audio. People are for the most part functionally illiterate, so this is huge; and of course they will capitalize on Android and other ecosystem-wide advantages the Alphabet empire has. Multimodal language-like models will obviously be table stakes in 2024. (Bytedance guy even hints that they'll opensource a model on Gemini's level.)
Interesting that one of people who had worked on aligning early Gemini said they had trouble aligning it – it burned through RLHF reward models, finding exploits and collapsing into gibberish (imagine using actual RLHF in 2023!). Maybe this has delayed the release, as well as the garden variety safetyism it has made more complex.
To be honest I was more excited about the other day's release of Mamba by Albert Gu and the legendary Tri Dao. There are many architectures that I expect will break through the Pareto frontier of a mature Transformer, but this one is the first that feels like an actual Vaswani et al. 2017 level advance. Unlimited context, here we come.
Hmm, it seems like I confused the MMMU and MMLU in my original post, despite knowing the difference. I'll edit accordingly.
The MMMU performance seems far more compelling compared to the latter, especially given Dean's methodology of zero-shotting both models.
As someone who is functionally literate, I certainly care more about text prowess, as I presume would most of the people here. But in terms of mundane value for the rest of the world, that will be handy.
Interesting/mildly concerning. I haven't heard any claims of such difficulty in early GPT-4 or Claude, but OAI is probably the best at "alignment" in general, while Anthropic gimps their models to hell.
I am the wrong person to comment on such architectural concerns, but if people I respect, such as you and some others, do stress its importance, I'm all for it.
Certainly it seems to me that context windows (along with hallucinations) are the biggest impediments in making LLMs useful for more tasks.
I wonder what the deeper implications for human cognition are. I don't think there are people who can keep 25k words in their working memory, that seems to be much smaller, but we certainly don't usually forget the start of a novella by the time we reach the end. Is there a lot of caching and summarization going on?
At any rate, I hope it beats the annoying reality that 128k and 200k context window models begin to severely underperform, especially for data presented in the middle.
How does it stack up to RWKV?
Yes, there is in effect a lot of "caching and summarization" going on -- although that's probably our 2023 ooga-booga, not-quite-wrong way of talking about something else. LLMs really only have their context window and it's feedback as a short-term memory. Which is fine for text translation, but is asinine if you want anything like a thinking engine. Goldfish with a notebook.
We and LLMs can both compress long stories into gists, but the LLMs just forget about it and repeat the work on every iteration. We remember the gists and use them as context on every iteration.
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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:
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.
In the first version of the paper, submitted for peer review, they went even harder:
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.
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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