Baldur BjarnasonWe used to have working spelling and grammar checkers. Why does everybody in tech pretend you need a whole-ass LLM to check for typos?
wikiyu@baldur
And translations
And text to speech was working good in most cases
Marcus Müller
Gustavo@qgustavor @wikiyu That's what I'd argue, too, but: this very basic theory and reality, especially of really available implementations, might diverge there.
Thing is that @baldur is actually someone from the field, so his word does weigh heavy to me, even if it doesn't reflect my own experience with translation quality.
(EDIT: way->weigh. Human in-mind translations are not perfect, either :D)
So, AFAICT and as best I know, in general LLMs are sensitive to the size of the training data set. Only a few languages have a collection of machine-readable texts big enough for these models
IIRC they used to compensate for this in the pre-LLM days specifically for each language.
Once everybody began to migrate to approaches that require large data sets, performance for all of those tasks (translation, summary, correction) in smaller languages especially began to suffer
Though, it should be noted that in a lot of third party, neutral testing, specialised models outperform LLMs for many language tasks such as summarisation, even in English. At least in the same ballpark, even if they underperform, while costing orders of magnitudes less