> It's not more consistent.
I think the parent was probably saying for a given input, Google MT provides the same output.
What is the value of temperature/variability in a LLM powered MT model?
I’d assume given the same inputs, you should only be given the best output.
I don't care about being given the exact same output (you're not getting deterministic translations from people either). I care about quality translations. Variability for GPT style translations is much more about word choice and style than wrong or extremely different translations. and if i really wanted to, i could guide both (word choice, style) either with instructions or examples.
ChatGPT translates ငါ့ကို ဒုတိယ အခွင့်ရေး တစ်ခု ပေးခဲ့တယ်။ as "I have received a second warning.", which is incorrect. အခွင့် https://en.wiktionary.org/wiki/%E1%80%A1%E1%80%81%E1%80%BD%E... does not mean "warning", even though that is a likely completion of "I have received a second " in English.
Google Translate gives me "Gave me a second chance.", which closely matches the Burmese sentence down to dropping the subject (common in Burmese, rare in English) which makes the translation sound weird.
So any claim that ChatGPT is better/worse at translating really needs to specify the languages involved and what your goal for the translation is. (E.g. the benchmark paper you link seems to focus on the ability to steer the translation by providing additional context.)
It’s not really a question of high resource vs low resource languages so much as what languages ended up in the training corpus.
1. There’s a lot of transfer learning going on with predict the next token LLMs. A model trained on 500B tokens of English and 50b tokens of French will speak French far better than if it was trained on only 50b tokens of French.
2. You don’t need parallel corpora for every single pair you want to translate to. This means that GPT LLMs only need single text data for the vast majority of languages. Training most NMT models you would need Burmese/English parallel data.
Both of the above combine to mean that not only is quality demonstrably better, amount of data needed is lower too.
GPT's burmese isn't worse because it's low resource. It's because open ai made no specific attempt to include burmese text.
They're not even trying. GPT-3 was 93% English with the 2nd biggest holder less than 2%
Neural Machine Translation models don't need those either. That's been the case at least since 2016.
That was one of the big claims by Google, that Google Translate had created its own "interlingua" thanks to which it could translate between languages for which it doesn't have parallel corpora:
Following several months of testing, the researchers behind the AI have seen it be able to blindly translate languages even if it's never studied one of the languages involved in the translation. "An example of this would be translations between Korean and Japanese where Korean⇄Japanese examples were not shown to the system," the Mike Schuster, from Google Brain wrote in a blogpost.
https://www.wired.co.uk/article/google-ai-language-create
It was obvious already back then that this "interlingua" was no other than English, e.g., from the same article:
The team said the system was able to make "reasonable" translations of the languages it had not been taught to translate. In one instance, a research paper published alongside the blog, says the AI was taught Portuguese→English and English→Spanish. It was then able to make translations between Portuguese→Spanish.
But, hey, hype.
Right. But those models still operate on parallel corpora alone.
In LLMs, translation pairs are not the only data improving translations.
>But, hey, hype
It's not hype man. GPT-4 is much better for distant language pairs.
Please translate this:
ငါ့ကို ဒုတိယ အခွင့်ရေး တစ်ခု ပေးခဲ့တယ်။
The sentence "ငါ့ကို ဒုတိယ အခွင့်ရေး တစ်ခု ပေးခဲ့တယ်။" translates to "They gave me a second chance." in English.
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. At least with NMT you know the errors are consistent.
I don't know who you know who thinks GPT is ahead, but nobody in the very well funded translation industry has a GPT powered translation engine for the key reason that it's not ready for production. For a human to post edit MT, we're mainly talking fixing broken vocab. You'd never present raw MT to a client. It needs editing. Heavily. To think LLM translation doesn't need editing is either coming from someone not in the industry, or blinded by hype. And the kind of editing issues are more insidious, like those found in voice dictated texts. Homophones aren't flagged by QA software because they are real words. Just like LLMs make real sentences, except when they don't, but good luck detecting that, and editing out the additional meaning it has decided to inject.
Have you tried to run the GPT-4 API on a segmented xliff at all? If the segmentation is bad, and full of tags, GPT4 breaks completely. It tries to close sentences that run across segments, it can't handle tags in-line (the chatgpt interface can, but you can't use that at scale).
It can do some impressive work, don't get me wrong, but I'm not sure how hands-on you've really been if you think it's a solved problem.
Production translation is a non-trivial output. 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? Or have you developed something that's still in stealth?
EDIT: Oh wow, all of your 107 submissions to HN from the 6 months your account has existed, have been about AI and LLMs. I guess I got the hype part right. As for industry knowledge the jury is still out, but this could well be the classic HN "I understand tech so obviously I understand everything" play. Keep us posted!
To be fair, this is an infamous failure mode of neural mtl too, and a big part of what makes the discourse around GPT so ... evocative of the discourse in 2017.
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.
Dude, don't be an asshole. You got useful experience. Help others with it instead of putting them down.
But I guess that would indeed sound much better around beers.
Translator for 20 years talking about using GPT for Japanese-English.
It is the case. Haven't seen anyone who uses both who thinks otherwise.
also https://arxiv.org/abs/2301.13294. This benchmarks 3.5 which is quite lot worse than 4 as it is against Google, NLLB, Deepl
here https://arxiv.org/pdf/2304.02210.pdf, GPT wins overwhelmingly with human evaluations. Seems like the typically evaluation models aren't really cutting it anymore especially BLEU
>Neural machine translation is just more consistent, doesn't hallucinate
It's not more consistent. The 2nd bit is just wrong lol.
One of the biggest complaints of Deepl is the tendency to make stuff up to make translations seem more natural.
Summarization and Translation are the tasks GPT models hallucinate the least.