[chaxor]

It's important to frame this correctly. The article is a bit misguided (it doesn't matter which university publishes an article) because there are so many ways in which a model can be altered, even excluding retraining weights. Also, even if the performance has dropped practically due to removing some resources for more shortcuts to be taken (for example changing beam search and typical sampling parameters), making implications about the outlook for the future is not really appropriate, since retraining weights, changing architecture, etc can improve capabilities immensely.

It's important not to suggest that GPT systems in general are on the way outside.ply due to some small alterations in parameters that make a system slightly less performant (which seems to be a popular perspective).