u/ryderdev

My team has basically never bothered with fine tuning models. The feedback loop is slow because you have to retrain. The model becomes out of date very quickly and in time there are corrections. And it just hasn't seemed to be worth the time and effort to integrate into our tools.

What we do instead is in-context learning with few-shot prompting. This gives us so much value for so little infrastructure that I'm having a hard time seeing the use case for fine tuning. I guess if you had a proprietary model or an open source model that you fine tuned and you could inference it for less money, that could probably work. But is it really going to be better than something like Haiku with good examples in context?

I would love to hear from anyone who is actually doing fine tuning to understand what I'm missing.

Genuinely curious. I work adjacent to translation (tech side) and from what I see on LinkedIn and in conversations with translators, the post-editing model seems universally hated. Translators say things like they end up rewriting most of it anyway but get paid a fraction of the rate, or they just breeze through it really quickly and accept that they can't give it the attention they would have liked to given the rate. Regardless, agencies keep pushing it as the future.

Is it actually working for anyone? Or is the whole model just optimizing for the agency's margin at the translator's expense?