Meta's Omnilingual MT for 1,600 Languages (opens in new tab)

Hello from Siem Reap, Cambodia! Awesome to see a fellow tech enthusiast from Cambodia.

I actually found Facebook’s translations pretty good (better than Google Translate for things longer than a sentence). From my understanding of Khmer, Khmer is a bit more verbose and context dependent, hence LLMs in Khmer would be a big help understand those nuances.

In the inverse case (LLMs generating khmer from English) I heard from locals that it sounds formal and “robotic” which I found quite interesting.

Kagi Translate is fantastic. Multilingual support is honestly one of the best things about LLMs, imo.

So, LLMs are noticeably better in Khmer than Google Translate? I wonder why Google Translate doesn't use Gemini under-the-hood. Perhaps it's more prone to hallucinations.

I'm interested in find some thorough testing of translations on different LLMs vs Translation APIs.

It's not even good for Chinese

*they're

(Sorry I had to)

Huh? I'm a native English speaker and the sentence looks OK.

Advances have demonstrated... The NLLB part is an adjectival(sic) phrase that modifies the noun "Advances".

Hopefully I'm not wrong...

Hey, this is super cool! I’ve been working on a similar problem, focusing on low-resource and underserved languages including the Mayan family, and have published some research and open resources around that [0, 1].

On the data side, I’ve found that the biggest bottleneck isn’t collecting text (it’s out there!) but reliable language identification. It’s often difficult or ambiguous to separate languages cleanly in datasets like Common Crawl, Fineweb, or others. I worked on improving this a bit for Fineweb 2 for my native language, that might inspire you [3].

Many of the challenges you mention seem to recur across regions and language families, so I’d love to connect and compare notes sometime. Feel free to reach me at omar [at] the labs site below.

0: https://wikilangs.org

1: https://omneitylabs.com

2: https://huggingface.co/blog/omarkamali/gherbal-multilingual-...

Common Crawl has been running a low-resource language project for 1.5 years now -- it's a hard problem.

It's sad that I didn't see any languages on your website from Australia, where there are hundreds of languages that need translating.

There’s many nation states working on this, have you looked into availability of those data sets?

What languages are you prioritizing?

It is not open weight as of today (unfortunately, for the reasons out of control of us the authors, we weren’t able to release the weights). All we could release is part of the evaluation data. I hope this will change in a while.

I haven't seen anywhere claiming they are open weight (although their last similar model, NLLB was).

They say their leaderboard and evaluation datasets are freely available. Closest statement I've seen in the paper, "Our translation models are built on top of freely available models."

It's an abstract for a paper, so it's officially supposed to be one paragraph.

WhatsApp has that feature!

Yes their translations offer negative value, which is annoying because at work you can't usually choose your locale settings.

And the errors are really basic, like translating shortly to short, not the same thing at all!

1.6k languages is for how many we were able to find more or less reliable evaluation data (mostly thanks to Bible translators and all those who contributed to BOUQuET). Out of the remaining several thousand languages, we expect the OMT models to support understanding (but not generation) for a significant proportion, due to cross-lingual generalisation between similar languages. So it’s not truly “omni” in the sense of supporting every single language on Earth, but it’s our best effort to do so, and probably the most “omni” models existing today.

when you market, you use frontier and edge terms, so it sounds pro max

I had the same reaction to this post. Mainly because one of Meta's explanations for the lapse was that they didn't have moderators who understood the local language

You hear that folks? Lack of localization kills.

Do you also boycott Toyota for the Hilux?

This project is absolutely NLLB 2.0 in spirit. However, we decided to reserve the name “OMT-NLLB” only to the subset of the new models that have encoder-decoder architecture similar to the original NLLB-200. The other models are called “OMT-LLaMA” and have classical LLM architecture. The idea here (and we had to emphasize it to justify the project internally) is that we are developing not just new models but a recipe for massive multilinguality that can be integrated into general-purpose LLMs.