And, ID, in the final analysis when you say "if it scores better it scores
better" you are begging the question.

That's less than not interesting.  It's a distraction.

Here's another way to talk about the situation that isn't a distraction:

"If it predicts better, it predicts better."

And now think about what it means to "predict better".

What is the essential function of a lossless compressor?

On Wed, Oct 7, 2020 at 9:56 AM James Bowery <[email protected]> wrote:

> How many times have I stated in this group that big language modelers
> bragging about their parameters counts worse than cottoncandyfluffpuffery?
> Reducing the parameter counts by a factor of 1000 and achieving comparable
> performance on just about _any_ plausible language benchmark is damning of
> such hypercottoncandyfluffpuffery.
>
> On Wed, Oct 7, 2020 at 4:08 AM <[email protected]> wrote:
>
>> James, if theirs is trained on enwik9 and if GPT was too, then we can
>> compare. Remember, if both models reached a training level where both
>> really need not eat more data, you'd see the smarter one is better no
>> matter how much data the other eats. So let's say GPT scores better than
>> theirs, it's ok if it has a big model, because the other can't reach its
>> accuracy even if eats to get a as big model as GPT's. Big model is only a
>> problem if can't run it. That requires optimization before moving on. One
>> day when we reach the seemingly best AI, we can then start worrying about
>> model size and speed as its the only gain left.
>>
>> And if I'm wrong, then my first thought on this thread is correct.
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