First thing to note. The best way to improve generalisability and accuracy is to have as accurate data as possible. If your data is trash, it doesn't matter how many parameters your classifier is using, it will not produce good results.

Now, in my experience using with transformer neural networks, If the task is a simple binary classification task or multi label with less than 8 or so labels(maybe more), the small models(14 million parameters) perform similar to the base models(110 million parameters). Once the objective function becomes more complicated, such as training a zero shot learner, more parameters means achieving a much lower loss. In the case just mentioned, using the large models(335 million parameters) had a significant improvement over the base model(110 million parameters).

It's hard to define and quantify how complicated an objective function is. But just know that the more parameters doesn't always mean better if the objective function is simple enough.