mcyc
Joined 6 years ago
Joined 6 years ago
Thanks for your comments here. I agree that a lot of labs were pretty unserious. One other thing that I should have mentioned in the article is that labs in Japan are like little contracting businesses, and the professors have to find funding in a way that seems much harder than the US. This leads to a lot of variability in the financial situations of labs, so yeah my concerns about funding/etc were probably understated (and my lab was likely one of the best funded in the nation).
As for domestic conferences, typically my lab used them as staging for their papers that they would send to top international conferences, so the quality was still pretty good. I do get the sense that the quality outside of the T1 labs drops off pretty quickly.
> ... but I never collaborated with them (in fact, our lab has hardly ever published a paper with >2 authors).
This was something I had in the original draft of the article, but it was removed for being a bit niche. TiTech (and perhaps other schools, but not universal) had a restriction in place where only one student could claim primary authorship on a paper for the purposes of their graduation requirements (typically 1 conference + 1 journal paper). Even if two students contributed equally, they had to sign a form designating one of them the "true" primary author. This massively discourages collaboration. I absolutely hated this policy, but there was basically nothing I could do at an institutional level to change it. I ended up being able to collaborate pretty freely, since I reached my publication requirements quickly, but this policy really hurts students who do their PhD in Japan just because of the numbers game. If two candidates are up for a position, and one has N papers (all first author, but 2-3 authors max) and the other has N + 10 papers (with <N first author, but all with huge groups), the one with N papers is at a disadvantage at first glance. And it just removes one of the primary skills you need to cultivate during a PhD: collaborating with other researchers.
Now that I think about it, one thing I am not sure about is if self-funded students' travel/conference fees were covered. I would guess they are covered through a grant and it is just the tuition that is self funded, but I do not know for certain. Going to >1 international conference a year would be far more than the tuition.
So, I would guess it would cost ~10k/year to be fully self funded. You can recoup some of that cost working (on a student visa this is limited though), and your lab may also have an RA stipend (but this is both very low [<$20/hr] and counts towards your 28 hour/week limit).
My advice though is to never do a self-funded PhD. Being a PhD "student" is a misnomer. You are a research employee and should be compensated as such.
What field were you in? I am surprised to hear that you think Japan's lab culture is much better than the US. I do think it varies a lot by lab, but overall the internal collaboration and guidance (if not from the PI, from senior members) was much better in American labs than in Japanese labs in my experience.
My decision to start my PhD in Korea was under very different circumstances. I was studying a totally different field (automata theory, so closer to mathematics than ML) where longer PhDs were normal, I was used to the US system, I wasn't working or married, etc. I expected it to take ~6 years when I started.
I basically had to choose between living in the Canadian countryside, Helsinki, or Seoul in order to be in a lab studying what I wanted. I was already pretty into Korean culture (I minored in Korean in college, studied abroad there, and was a serious StarCraft player) so that choice was easy!
When I started looking again, I knew I wanted a shorter PhD (especially since I had a masters and industry + industry research experience), so Japan was pretty attractive.
I will admit that I think 3 years is a little short to make substantive progress outside of machine learning, so unless you come in with a lot of research experience and a good idea of what you want to work on, I think a longer PhD would be beneficial, but the opportunity cost is so high that it really doesn't seem like a great option on paper.
What field were you supervising?
3 years in CS is, imo, a little tight but not bad. But I always wondered how people in other fields (stem and otherwise) did it. CS simply doesn't have to deal with the extraneous factors that come with, e.g., biology or climatology.
One thing to note is that, after I submitted the final draft of this article, the Japanese government ended the MEXT university track or is at least planning to. There was a memo, but the status is not clear.
Anyway, I had a great time doing my PhD this way, and generally I recommend that people work before a PhD and try to work during it (if you are in CS). It gives you a lot of freedom and, at least for me, it is very helpful to have more than one pipeline of work at a time so that when one slows down (as it inevitably does in research), you can draw on the other.
It requires you to solve a mate-in-one puzzle to, e.g., post on the forums.
(Sorry, don't have a better link, there wasn't any non-technical I could find about it).
https://www.reddit.com/r/chess/comments/q19wgq/til_lichess_d...
```
Llama3
english, 0.216
portuguese, 0.285
italian, 0.287
greek, 0.592
```
```
Gemma4
english, 0.219
portuguese, 0.246
italian, 0.249
greek, 0.537
```
```
Kimi2.6
english, 0.214
portuguese, 0.310
italian, 0.308
greek, 0.716
```
Portuguese is worse than English certainly, but it is on par with Italian (which I think has more overlap with English) and much better than Greek (since it doesn't use the Latin script and is definitely not prioritized in the tokenizer construction).
On your second point, tokenizer transfer allows for extending/modifying a tokenizer without retraining the model from scratch. The simplest version of this is tokenizer extension + continual pretraining, where you just add a bunch more tokens to the vocab for the language/domain that you want to improve and train a little more. It's been done for Japanese [2] and Indic languages, but afaik not Portuguese.
So I think that continual pretraining for a large base model would have probably been fine for this case with huge cost savings. But it is good to have the ability to train your own base models, so I don't think this is such a bad idea.
-----------------------
[1]: https://huggingface.co/datasets/goldfish-models/fish-food
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