Comment by og_kalu
2 years ago
>on what basis? What makes a predictor LLM better at translating Latin than a system trained specifically for translation?
They just are. Sure it sounds a bit strange if you've never thought about it but they are.
>I'm sure they can do a decent job but it's weird to me that someone would leap to GPT-style tech despite its known tendency to hallucinate/make stuff up instead of translation-oriented tools like DeepL or Google Translate
1. They don't just potentially do a decent job. For a couple dozen languages, GPT-4 is by far the best translator you can get your hands on. Google, Deepl are not as good.
2. Tasks like summarization and translation have very low hallucination rates. Not something to be particularly worried about with languages that have sufficient presence in training.
>I can't imagine there are vast swaths of Latin in GPT's training set.
Doesn't matter. There is incredible generalization for predict the next token models as far as proficiency is concerned. a model trained on 500b tokens on English and 50b tokens of french will not speak french like a model trained on only 50b tokens of french but much much better.
https://arxiv.org/abs/2108.13349
It also doesn't need to see translation pairs for every language in its corpus to learn how to translate that language pair(but this is the case for traditional models too)
> a model trained on 500b tokens on English and 50b tokens of french will not speak french like a model trained on only 50b tokens of french but much much better.
Thats because french and english are reasonably similar, and share the same context.
Whilst latin and english are distantly related (latin is more related to french) they do not share the same cultural context.
Which version of latin are you translating? medieval?
Whilst its fun to do, and it has its place. There needs to be massive caveats about accuracy.
It doesn't matter whether it's French or Korean. That was just an illustration.