Training is what makes neural networks faster, language usage makes one fluent and English’s frequent usage speeds up generative translation.

why not to test different language with similar and repeated tasks?

The language inter-operability is improving every movement for many models.

I think that depends to the language not the LLM. they do not translate, but if a lnaguage express same thing with longer tokens that counts.

Use different tokenizers for different languages. Or at least, use different tokenizers for different scripts.

Isn't English one of the most information dense languages?

The choice to measure in “energy” instead of just “cost” suggests to me this is an unserious attempt at activism instead of a worthwhile benchmarks of LLMs.

Also: Gemma 3, Llama 3.1, come on…

English is the most climate-friendly language, yay!

How does Multi-Token Prediction factor into this?

I suspect the quality might be higher too when using more energy.

Recently, while I was doing a presentation on LLMs at work, I got the question whether prompting in Dutch was fine, or whether English was better. I made the argument that English was probably better, since much more of the content it was trained in was probably English than Dutch and therefore I would assume it would have better understanding of it. I never considered this would also have an impact on the amount of energy being used. Pretty interesting.

This is what I have been proposing all along:

Ban any language other than English from prompting.

This has multiple benefits - first you restrict third world from spamming USA data centers. Why would America pay for some guy in India using free model?

This alone is worth considering but also you can optimise LLMs further for a single language as opposed to having to translate and couple weights for every single concept.

Why would American LLMs support Chinese language or Arabic? Make them learn English and come to work for us. No more free stuff for the third world.