Meta just claimed the opposite in their Llama 3.1 paper. Look at the conclusion. They say that their experience indicates significant gains for the next iteration of models.
The current crop of benchmarks might not reflect these gains, by the way.
The current crop of benchmarks might not reflect these gains, by the way.
Anyway here's the sales page. the widget subscription is so premium you won't even miss the subscription fee.
We should still be skeptical because often want to claim to be better or have unearned answers, but I don't think the motive to lie is quite as strong as a salesman's.
It's not peer-reviewable in any shape or form.
That said, doing that is slow and people will need to make decisions before that is done.
It's weird. In the late 2010s it seems like people were wising up to the idea that you can't implicitly trust big tech companies, even if they have nap pods in the office and have their first day employees wear funny hats. Then ChatGPT lands and everyone is back to fully trusting these companies when they say they are mere months from turning the world upside down with their AI, which they say every month for the last 12-24 months.
In the 2000s we only had Microsoft, and none of us were confused as to whether to trust Bill Gates or not...
https://www.wheresyoured.at/pop-culture/
> What makes this interview – and really, this paper — so remarkable is how thoroughly and aggressively it attacks every bit of marketing collateral the AI movement has. Acemoglu specifically questions the belief that AI models will simply get more powerful as we throw more data and GPU capacity at them, and specifically ask a question: what does it mean to "double AI's capabilities"? How does that actually make something like, say, a customer service rep better? And this is a specific problem with the AI fantasists' spiel. They heavily rely on the idea that not only will these large language models (LLMs) get more powerful, but that getting more powerful will somehow grant it the power to do...something. As Acemoglu says, "what does it mean to double AI's capabilities?"
I’m about Zucks age, and have been following his career/impact since college; it’s been roughly a cosine graph of doing good or evil over time :) I think we’re at 2pi by now, and if you are correct maybe it hockey-sticks up and to the right. I hope so.
If LLMs end up being the platform of the future, Zuck doesn't want OpenAI/Microsoft to be able to monopolize it.
> Other companies sell widgets. We have a bunch of widget-making machines and so we released a whole bunch of free widgets. We noticed that the widgets got better the more we made and expect widgets to become even better in future. Anyway here's the free download.
Given that Meta isn't actually selling their models?
Your response might make sense if it were to something OpenAI or Anthropic said, but as is I can't say I follow the analogy.
- flex
- deal a blow to Altmann
But it has nothing to do with LLMs (and interestingly enough they aren't opening their recommendation tech).
They are by no means bad, but I am now mostly interested in long context competency. We need benchmarks that force the LLM to complete multiple tasks simultaneously in one super long session.
I mean, going by their own model evals on various benchmarks (https://llama.meta.com/), Llama 405b scores anywhere from a few points to almost 10 points more than than Llama 70b even though the former has ~5.5x more params. As far as scale in concerned, the relationship isn't even linear.
Which in most cases makes sense, you obviously can't get a 200% on these benchmarks, so if the smaller model is already at ~95% or whatever then there isn't much room for improvement. There is, however, the GPQA benchmark. Whereas Llama 70b scores ~47%, Llama 405b only scores ~51%. That's not a huge improvement despite the significant difference in size.
Most likely, we're going to see improvements in small model performance by way of better data. Otherwise though, I fail to see how we're supposed to get significantly better model performance by way of scale when the relationship between model size and benchmark scores is nowhere near linear. I really wish someone who's team "scale is all you need" could help me see what I'm missing.
And of course we might find some breakthrough that enables actual reasoning in models or whatever, but I find that purely speculative at this point, anything but inevitable.