So, given the anglo-centrism of current models, my confidence in American providers giving any shits about non-american users/use-cases is pretty low. And lower the smaller the language community is.
If you’re doing this on a daily basis, then you should have an AGENTS.md that accumulates directional instructions like this.
This is how you use the tool correctly.
There’s this weird pattern I’ve noticed where people expect LLMs to require zero effort or proficiency on their part, and when the LLM isn’t perfect without it, of course it wasn’t; LLMs suck.
Very very emphatic agree from my end, thanks.
Then add top-level instructions saying what country you're from, what country you live in now, and which language you speak. This isn't that hard.
Most ordinary people will just use their native language and they have no way of knowing that the model always reasons in English and therefore is strongly biased toward using English search terms. So they don't know they have to remind the model to search in their local language.
I have the opposite problem, where I'll ask in English, about something in a foreign country, the results it finds will all be in that foreign language, and the LLM will switch languages and respond in that language (which I don't speak).
So then I have to ask it "can you repeat that in English please."
I keep waiting for the new GPT-Definitelty-AGI-For-Real-This-Time to fix it but it's still there.
not necessarily. i often prompt Claude in German and then see the reasoning happening in English. of course it will eventually reply in German, but that does not mean that the tooling in the background was using German.
I was trying to work out how and when to use swear words, and the relative power index of them. it translated english swear words into the target language then lectured me on not using them.
It took a bunch of prodding for it to actually think as the target language to then get the (mostly) correct response.
Not kidding at all. I had a similar issue with a project where I needed to classify images into specific demographics, and Gemini, while capable, was entirely not going to do the task… until in my JSON response I left room for it to tell me why this was not a good idea and why it was culturally insensitive. Then boom… full JSON array: hair color, eye color, skin color, fitness level, likely ethnicity, likely country of origin, and about 10 other values.
You’re probably wondering what on earth I was working on. I was matching Ai gen headshots to Ai voices so that in an app the voice picker had human (Ai) faces.
I wonder how much of this is also just the search engine's region setting.
It's a big problem I regularly have with Google. I almost always want English language, US-centric results, so I have my region set to the US. But occasionally I want results relevant to my actual country, and even searching in my native language usually yields much worse results than just opening an incognito tab and letting it default to my real location.
To this day they still think I'm in Sweden sometimes, in Paris other times, or in Germany, while I haven't lived in any of those places for years.
Which is bizarre to me Norway doesn't have a booming tech sector with all hat wealth fund acting as the biggest VC.
They instead use their wealth fund to invest in US's tech sector. Baffling.
Even Norway themselves admit they're the underperformers of the Nordics. https://skywlkr.no/wp-content/uploads/2019/10/TechScaleupNor...
So blaming population is a cheap excuse that doesn't hold water. Especially that you can always import the skilled people you lack, when you have virtually unlimited money and some of the highest standards of living in the world.
It would create jobs, sovereignty, intellectual property and soft power?
Instead it goes to strengthening the tech monopoly of a country that threatens to invade your neighbour.
Norway has a manpower bottleneck. The UK had spent its oil windfall domestically and it barely registered. But for a nation of then some 4 million the economy melts down with so much monetary mass.
You make it sound like an easier task than training an LLM. I'd argue it's not obvious, and would assume the contrary.
With absolutely no insight into why, which one has better odds to happen first is obvious to me.
Having insights into both translations, transcriptions and attempting to build LLMs myself, I'm fairly sure which effort would be successful first, regardless of how many attempt it first.
Translation is never a bijective process. It's never quite the same experience in translation as it is in the original, due to the cultural differences between reader and writer. Larger in this case because 1930s Norway is very different even from 2020s Norway.
Ultimately this was not a success due to marketing difficulties; it is very difficult to get a book noticed.
( https://www.amazon.co.uk/Iron-Chariot-Nordic-Crime-Library/d... )
I just think building a LLM from scratch is ever harder, with more potential problems that are harder to solve, more time-consuming and even more resource-intensive.
Why would it be easier in the future? The advances we see with LLMs today require a huge amount of data, and it's getting hard getting the amount of data just using any language, I'm having a hard time seeing how it'd get easier for Norwegians to build their own LLM, unless they seriously start to ramp up how much Norwegian content they're putting out.
> If we need to translate everything to English we might as well just drop using Norwegian altogether. Practically everyone speaks English fluently already...
Yeah I mean with that black and white perspective you can pretty much do anything and it won't matter for anything :) I think for the rest of us, what we speak daily and what we rely on professionally, can differ, and that's OK. But maybe this is just my broken Swedish mind being so used to using English professionally but then conversing in Spanish outside of work daily, YMMV.
Furthermore one can reuse investments in data (both agreements, infrastructure and datasets), compute (GPUs, servers) and know-how (training scripts, experienced engineers).
I understand and agree building the LLMs yourself comes with more benefits, long-term ones especially, but still it's harder, more expensive and really time consuming work.
But for a national lab I think it is money well spent to figure out the possibilities and limitations of a native-language LLMs for languages with order of 5M-10M speakers.
Nynorsk and bokmål is not dialects but variants of written Norwegian.
Only if you believe other people will value that enough to expend the effort necessary to use it. If you believe other people will see it as low value and ignore it then you'd be better off doing the training yourself in order to guarantee it happens.
There's also a secondary benefit that your team doing the work will learn some useful skills while they do it.
I mean it's their job to give people access to information, and they certainly do, but the mark of a professional, in their eyes, is guarding information. It's much more embarrassing for them professionally to give too much access than too little.
LLM training gives them a "respectable" way of bypassing that and give the world their information (which, in fairness, they probably all really want to do if they could).
Uuh.. No? Especially of the training data, as in this case, is of better quality.
Answer: idiocy of decision makers and the desire to get resources by those who created the proposal.
I assumed Scandinavia has better decision processes but apparently I was wrong.