5 ms·

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 iterati

by __jl__ 2y ago

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.

nathanasmith 2y ago
They also said in the paper that 405B was only trained to "compute-optimal" unlike the smaller models that were trained well past that point indicating the larger model still had some runway so had they continued it would have kept getting stronger.
- moffkalast 2y ago
  Makes sense right? Otherwise why make a model so large that nobody can conceivably run it if not to optimize for performance on a limited dataset/compute? It was always a distillation source model, not a production one.
imtringued 2y ago
LLMs are reaching saturation on even some of the latest benchmarks and yet I am still a little disappointed by how they perform in practice. 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.
- xeromal 2y ago
  I don't know anything about AI but there's one thing I want it to do for me. Program a full body exercise program long term based on the parameters I give it such as available equipment and past workout context goals. I haven't had good success with chatgpt but I assume what you're talking about is relevant to my goals.
  - ThrowawayTestr 2y ago
    Aren't there apps that already do this like Fitbod?
    - xeromal 2y ago
      Fitbod might do the trick. Thanks! The availability of equipment was a difficult thing for me to incorporate into a fitness program.
splwjs 2y ago
I sell widgets. I promise the incalculable power of widgets has yet to be unleashed on the world, but it is tremendous and awesome and we should all be very afraid of widgets taking over the world because I can't see how they won't. Anyway here's the sales page. the widget subscription is so premium you won't even miss the subscription fee.
- sqeaky 2y ago
  That is strong (and fun) point, but this is peer reviewable and has more open collaboration elements than purely selling widgets. 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.
  - troupo 2y ago
    > this is peer reviewable It's not peer-reviewable in any shape or form.
    - hnfong 2y ago
      It is kind of "peer-reviewable" in the "Elon Musk vs Yann LeCun" form, but I doubt that the original commenter meant this.
    - sqeaky 2y ago
      Others can build models that try to have decent performance with a lower number of parameters. If they match what is in the paper that is the crudest form of review, but Mistral is releasing some models (this one?) so this can get more nuanced if needs. That said, doing that is slow and people will need to make decisions before that is done.
      - troupo 2y ago
        So, the best you can do is "the crudest form of review"?
- 2y ago
dev1ycan 2y ago
Or maybe they just want to avoid getting sued by shareholders for dumping so much money into unproven technology that ended up being the same or worse than the competitor
Bjorkbat 2y ago
Yeah, but what does that actually mean? That if they had simply doubled the parameters on Llama 405b it would score way better on benchmarks and become the new state-of-the-art by a long mile? I mean, going by their own model evals on various benchmarks (https://llama.meta.com/ 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.

by __jl__ 2y ago

nathanasmith 2y ago

They also said in the paper that 405B was only trained to "compute-optimal" unlike the smaller models that were trained well past that point indicating the larger model still had some runway so had they continued it would have kept getting stronger.

moffkalast 2y ago
Makes sense right? Otherwise why make a model so large that nobody can conceivably run it if not to optimize for performance on a limited dataset/compute? It was always a distillation source model, not a production one.

imtringued 2y ago

LLMs are reaching saturation on even some of the latest benchmarks and yet I am still a little disappointed by how they perform in practice. 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.

xeromal 2y ago
I don't know anything about AI but there's one thing I want it to do for me. Program a full body exercise program long term based on the parameters I give it such as available equipment and past workout context goals. I haven't had good success with chatgpt but I assume what you're talking about is relevant to my goals.
- ThrowawayTestr 2y ago
  Aren't there apps that already do this like Fitbod?
  - xeromal 2y ago
    Fitbod might do the trick. Thanks! The availability of equipment was a difficult thing for me to incorporate into a fitness program.

splwjs 2y ago

I sell widgets. I promise the incalculable power of widgets has yet to be unleashed on the world, but it is tremendous and awesome and we should all be very afraid of widgets taking over the world because I can't see how they won't. Anyway here's the sales page. the widget subscription is so premium you won't even miss the subscription fee.

sqeaky 2y ago
That is strong (and fun) point, but this is peer reviewable and has more open collaboration elements than purely selling widgets. 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.
- troupo 2y ago
  > this is peer reviewable It's not peer-reviewable in any shape or form.
  - hnfong 2y ago
    It is kind of "peer-reviewable" in the "Elon Musk vs Yann LeCun" form, but I doubt that the original commenter meant this.
  - sqeaky 2y ago
    Others can build models that try to have decent performance with a lower number of parameters. If they match what is in the paper that is the crudest form of review, but Mistral is releasing some models (this one?) so this can get more nuanced if needs. That said, doing that is slow and people will need to make decisions before that is done.
    - troupo 2y ago
      So, the best you can do is "the crudest form of review"?
2y ago

dev1ycan 2y ago

Or maybe they just want to avoid getting sued by shareholders for dumping so much money into unproven technology that ended up being the same or worse than the competitor

Bjorkbat 2y ago

Yeah, but what does that actually mean? That if they had simply doubled the parameters on Llama 405b it would score way better on benchmarks and become the new state-of-the-art by a long mile? I mean, going by their own model evals on various benchmarks (https://llama.meta.com/ 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.