Before revision:  While fine-tuning large language models usually gives the best performance, in many applications it is preferable to tune much smaller sets of parameters, so that the majority of parameters can be shared across multiple tasks . The main approach is to train one or more task-specific layers on top of the language model.
After revision:  A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model.