These days large language models cover a vast amount of topics and information, but I wanted to understand: For specific tasks, is it better to fine-tune models on examples or just use prompting with the context of the task?
For example, if I wanted to train a language model to do question answering for linear algebra, is it better to train it with examples of linear algebra problems and their solutions, or try out different prompts?
Fine tuning is superior, since the whole network specialise to solve a given problem only. A specialist will always beat a generalist in the specialised task.
That said, if the generalist network is large enough and the prompt is good enough to "turn-on" the right weights in the large network, the performance can be comparable. In a sense, the prompt is making the generalist network act as a specialist. This is assuming that the specialist network has saturated its performances.
The paper The Power of Scale for Parameter-Efficient Prompt Tuning shows indeed how "Prompt Tuning" can be as good as fine tuning for large networks.
