Idiomatic translation of text matching a human professional (e.g. free of errors for legal terms, interesting and natural for fiction) is unlikely to be achieved until we have AGI. So no.
I'm sure it'll be doing that in five years, but not now.
One interesting thing is that's it's nondeterministic, so sometimes 'For chrissakes' turns to ちくしょう (Damn!) but sometimes to クリスのために (for Chris' sake). Sometimes 'the goddamn door' turns into クソドア ('shit door'), sometimes the goddamn changes the phrasing of the whole sentence instead. If you run it five times and take the best sentences out of all five runs it's probably quite good. Maybe prompting would help too, I said "idiomatic Japanese" but it still usually translated it in a very "foreigner Japanese" way typical of US drama/movie translations.
In "Can GPT-4 translate literature?" (Mar 18, 2023) [https://youtu.be/5KKDCp3OaMo?t=377], Tom Gally, a former professional translator and current professor at the University of Tokyo, said:
> …the point is, to my eye at least, the the basic quality of the translation [from Japanese to English] is as good as a human might do, and with some relatively mild editing by a sensitive human editor, it could be turned into a novel that would be readable and enjoyable.
Like he says, there's lots of ambiguity in Japanese that needs to be handled, gender not being specified until later, etc. and an editor would need to spend time going over it - but it saves months of traditional work. There are words and _concepts_ that are hard to translate, there are cultural issues, dialects, slang, registers. So yeah it'll make the media accessible, but it won't be as a good as a skilled translator.
I mean i can if you want (chinese though) but enough people lie on the internet.
I'm a native speaker of Chinese, but not a professional translator.
and you can see talk on English/Japanese here - https://youtu.be/5KKDCp3OaMo?t=377
This.
GPT/ChatGPT is able to even translate between different "accents" or dialects of the same language. For example it can give you the same sentence in Mexican, Argentinean or Peruvian Spanish.
Example:
Me: Give me a sentence in spanish that is different in Mexican, Argentinean and Peruvian Spanish. Write the sentence in each dialect.
ChatGPT: Mexican Spanish: "¿Qué onda, güey? ¿Cómo estás?" Argentinean Spanish: "¿Qué onda, boludo? ¿Cómo estás?" Peruvian Spanish: "¿Qué tal, causa? ¿Cómo estás?"
These sentences mean "What's up, dude? How are you?" in English. The primary difference is the slang term used for "dude" or "friend" in each dialect: "güey" in Mexican Spanish, "boludo" in Argentinean Spanish, and "causa" in Peruvian Spanish.
Similar to how a lot of swear words work in many languages.
It's interesting to see how what matters is not the word, but the intention behind it. At the end we are trying to communicate meaning, and words are just one of our tools to do it.
Of Course i don't know the data for GPT-4
Mandarin isn't the second language I speak but I officially compared with it because I wanted to test also with a model that had more equivalent corpus training than the very lopsided gpt models. And Chinese/English is the only combo that has a model of note in that regard.
For almost everything else, it seems to produce pretty decent and usable translations, even when used against relatively obscure languages.
I used it a some green landic article that was posted on hn yesterday (about Greenland having gotten rid of daylight saving time). I don't speak a word of that language but the resulting English translation looked like it matched the topic and generally read like correct and sensible English. I can't vouch for the correctness obviously. But I could not spot any weird errors or strange formulations that e.g. Google translate suffers from. That matches my earlier experience trying to get chat gpt to answer in some Dutch dialects, Frysian, Latin, and a few other more obscure outputs. It does all of that. Getting it to use pirate speak is actually quite funny.
The reason that I used Chat GPT for this is that Google translate does not understand greenlandic. Understandable because there are only a few tens of thousands of native speakers of that language and presumably there's not a very large amount of training material in that language.
Therein lies the rub. There's a huge gap between what LLMs can currently do (spit back something in a target language that gives you the basic idea, however awkwardly phrased, of what was said in the source language). And what is actually needed for idiomatic, reasonably error-free translation.
By "reasonably error-free" I mean, say, requiring a human correction for less than 5 percent of all sentences. Current LLMs are nowhere near that level, even for resource-rich language pairs.
I ran the abstract of this article through chat gpt. Flawless translation as far as I can see. To be fair, Google translate also did a decent job. Here's the chat GPT translation.
Veel NLP-toepassingen vereisen handmatige gegevensannotaties voor verschillende taken, met name om classificatoren te trainen of de prestaties van ongesuperviseerde modellen te evalueren. Afhankelijk van de omvang en complexiteit van de taken kunnen deze worden uitgevoerd door crowd-werkers op platforms zoals MTurk, evenals getrainde annotatoren, zoals onderzoeksassistenten. Met behulp van een steekproef van 2.382 tweets laten we zien dat ChatGPT beter presteert dan crowd-werkers voor verschillende annotatietaken, waaronder relevantie, standpunt, onderwerpen en frames detectie. Specifiek is de zero-shot nauwkeurigheid van ChatGPT hoger dan die van crowd-werkers voor vier van de vijf taken, terwijl de intercoder overeenkomst van ChatGPT hoger is dan die van zowel crowd-werkers als getrainde annotatoren voor alle taken. Bovendien is de per-annotatiekosten van ChatGPT minder dan $0.003, ongeveer twintig keer goedkoper dan MTurk. Deze resultaten tonen het potentieel van grote taalmodellen om de efficiëntie van tekstclassificatie drastisch te verhogen.
Translating the Dutch back to English using Google translate (to rule out model bias) you get something that is very close to the original that is still correct:
Many NLP applications require manual data annotations for various tasks, especially to train classifiers or evaluate the performance of unsupervised models. Depending on the size and complexity of the tasks, these can be performed by crowd workers on platforms such as MTurk, as well as trained annotators, such as research assistants. Using a sample of 2,382 tweets, we show that ChatGPT outperforms crowd workers for several annotation tasks, including relevance, point of view, topics, and frames detection. Specifically, ChatGPT's zero-shot accuracy is higher than crowd workers for four of the five tasks, while ChatGPT's intercoder agreement is higher than both crowd workers and trained annotators for all tasks. In addition, ChatGPT's per-annotation cost is less than $0.003, about twenty times cheaper than MTurk. These results show the potential of large language models to dramatically increase the efficiency of text classification.
I'm sure there are edge cases where you can argue the merits of some of the translations but it's generally pretty good and usable.
I will be re-assessing my view on general-case translation performance accordingly.