Re:Playing Around with Machine Translation
by og_kalu on Thursday September 14, 2023 @07:02PM
(#37515956)
>It may present more fluent text, but if it doesn't know it's strayed from the source text and you can't tell either (because you don't understand source language) then you'll end up with error-laden pseudo translations.
I understand Korean. I've tested it there too and my evaluations haven't changed. It's better. A lot better.
That aside, nothing you've said here doesn't apply to NMT.
I'm going to be Frank here. You don't what you're talking about when it comes NMT and it makes this conversation very frustrating. You keep mentioning cons that apply to NMT as well.
Literally almost all SOTA NMT models (NLLB for instance) today are Transformers or some transformer variant (In fact this was the original proposition in the famous Attention Paper). They're just transformers trained for a translation primary objective.
Those cons of GPT, NMT inherit them too. They hallucinate, they can make pseudo translations etc.
>EDIT: Oh wow, all of your 107 submissions to HN from the 6 months your account has existed, have been about AI and LLMs.
So ? That's what I like to do. Is this some attempt at an ad hominem?
The 2nd paper I linked is about document level translations.
>The entire industry hasn't released an LLM solution yet for translation (excepting the rewording mini features). What makes you think you know more than those on the ground?
Training and inference for GPT scale models is extremely expensive.
I understand Korean. I've tested it there too and my evaluations haven't changed. It's better. A lot better.
That aside, nothing you've said here doesn't apply to NMT.
I'm going to be Frank here. You don't what you're talking about when it comes NMT and it makes this conversation very frustrating. You keep mentioning cons that apply to NMT as well.
Literally almost all SOTA NMT models (NLLB for instance) today are Transformers or some transformer variant (In fact this was the original proposition in the famous Attention Paper). They're just transformers trained for a translation primary objective.
Those cons of GPT, NMT inherit them too. They hallucinate, they can make pseudo translations etc.
>EDIT: Oh wow, all of your 107 submissions to HN from the 6 months your account has existed, have been about AI and LLMs.
So ? That's what I like to do. Is this some attempt at an ad hominem?
The 2nd paper I linked is about document level translations.
>The entire industry hasn't released an LLM solution yet for translation (excepting the rewording mini features). What makes you think you know more than those on the ground?
Training and inference for GPT scale models is extremely expensive.