11–20 of 127 posts
Yeah this is old news. When i was in China last year google translate seemed to literally never work (looks of confusion trying to do basic interactions in stores). GPT-4 worked perfectly every time, I think google translate might be another soft abandoned project from google
GPT-4 is much more natural.
I still find it amazing how LLM translation capabilities are an almost accidental feature. Yet they still managed to leapfrog decades of research and billions of investment dollars in traditional machine translation.
Didn't "Attention Is All you Need" bill transformers primarily as a translation model?
But the main instructions were two types - summarise x, and translate y to z.
Translation in many ways was the root of seeing that multiple tasks/instructions could fit a model and not just in the context of multiple training loss methods, like NSP vs Gap filling(a modus of training difference, not actual task itself).
And the special tokens for BERT etc trace their origins back to enabling the task above and positional embeddings(and encodings). Which largely trace their origin to translation work.
ChatGPT 3 was already far beyond google translate in my attempts, mostly using it between English, Danish and Swedish. Even being able to ask it to translate song lyrics while retaining rhyming structure was sometimes not too bad :)
I still find it amazing how LLM translation capabilities are an almost accidental feature. Yet they still managed to leapfrog decades of research and billions of investment dollars in traditional machine translation.
Every other use case is basically an accidental feature
ChatGPT is still better due to it being less castrated and less likely to actually refuse my commands, but Claude works very well with text, when it works.
I still find it amazing how LLM translation capabilities are an almost accidental feature. Yet they still managed to leapfrog decades of research and billions of investment dollars in traditional machine translation.
(Edit: To clarify, maintaining semantics is held sacrosanct in classical methods of machine translation)
Earlier quoted context omitted.
Didn't "Attention Is All you Need" bill transformers primarily as a translation model?
Yes, but the translation model in the “Attention is All You Need” was trained on parallel sentences (input: English sentence, output: French). LLMs such as Claude are not trained on any labeled data. Yet, they still surpass the special-case models.
Yeah this is old news. When i was in China last year google translate seemed to literally never work (looks of confusion trying to do basic interactions in stores). GPT-4 worked perfectly every time, I think google translate might be another soft abandoned project from google
Google Translate simply doesn't use a GPU for translation. It's as easy as that. There is a huge jump in cost associated with using the GPU.
You don’t just “use a gpu.” Software doesnt get better by magically throwing it at a gpu. Moreover you can’t just run any old software on a gpu, it has to be built for it.
Even then, the gpu is just a speed increase, not a magical make better box.
That’s like saying any random text editor would be a better translator if they ran on GPUs.
EDIT: google also literally builds its own acceleration hardware, suggesting that they can’t afford GPUs (which they already own for GCP) for google translate is weird.