My understanding is generally this is not considered an obvious result. In that high parameter generalist models largely outperform lower parameter specialists.
The real issue is they tested on data in their training set. *
* Incorrect-- Edit misread parent comment.
Hm, no.
They trained on a part of their synthetic set and tested on another part of the set. Or at least that's what they said they did:
> from which 1,000 were held out as a benchmark test set.
Emphasis mine.
The difference is subtle but important. If we expect the model to truly outperform a general model, it should generalize to a completely independent set.
It's worth pointing out that that's technically not testing on the training set, but looking at how similar examples are in the dataset, it's clear that severe overfitting would be unavoidable. That also makes the headline very misleading.
The weights may not be published since using it for document extraction on even the same format but with slightly different content or lengths would show how abysmal this finetune does outside of the synthetic data.