I'm applying this to Mandarin (link in profile), and while there might not be much publicity/hype, there's definitely been an avalanche of people doing the same thing.
Current problems to solve:
- even the "best" LLM (GPT-4) frequently generates explanations/grammar that are plain wrong. Even its "correct" output isn't quite native (not as bad as round-tripping a sentence through Google Translate, but it's just slightly off).
- LLMs from Chinese companies (Qwen/Baichuan/etc) are immensely better at producing natural Mandarin (but fall short in other respects, which is unsurprising because they're smaller). I haven't tried fine-tuning LLaMa-2 yet, but I've had good success fine-tuning Qwen.
- in my opinion, 90% of the market doesn't need open-ended conversation about random topics. They need structured content with a gradual progression and regular review. You can use LLMs to generate this (which is what I'm doing), but it's not like a random newbie student is going to be able to design this themselves.
Not saying any of these problems aren't solvable, just pointing out the work that still needs to be done.
For me, the most exciting prospect is automated grammar correction during spoken conversation. I've made things harder for myself because I wanted to keep everything on-device so users could be assured that if they purchase something, they'll have access to it forever[0]. The downside is that I can't (yet) practically deploy any of these cutting-edge LLMs at the edge so I'm kind of handicapped in what I can do.
[0] subject to iOS/Android forced upgrades, which I have no control over. It's all cross-platform though, so I'll make a macOS/Linux/Windows version available at some point.