How can pre-trained language models be adapted for tasks beyond NLP?

Pre-trained language models can be adapted for various tasks beyond traditional NLP through techniques like transfer learning, where the model is fine-tuned on a specific dataset relevant to the new task. This involves adjusting the weights to minimize the loss specific to the target task while leveraging the general language understanding acquired during pre-training. Additionally, approaches such as prompt engineering and few-shot learning enable models to perform tasks with minimal additional training or examples.