You asked a good question. Supervised Instruction Fine-Tuning (SFT) is a way to make language models better at learning new things without seeing any examples by teaching them different tasks using words. For example, a language model can learn how to do things like finding out how people feel, making short summaries, answering questions, etc. by following the words that explain each task.

SFT has been proven to make language models much better at doing new tasks that can be explained using words. For instance, FLAN, a very big language model that learned over 60 NLP tasks using words, does better than GPT-3, another very big language model, on 20 of 25 tasks that it tried.

However, SFT is not the final goal of Natural Language Processing (NLP) field. There are still many problems and limits that SFT and other language models have, such as:

- SFT needs a lot of different and good quality data that teaches it using words, which may not exist or be easy to get for some topics or languages.
- SFT may not do well on tasks that are hard to explain using words or that need more complicated thinking or common sense knowledge.
- SFT may not be able to deal with different kinds of inputs or outputs, such as pictures, sounds, or videos, which are very important for NLP uses.
- SFT may not be able to make sure the safety, fairness, or ethics of the language models' outputs, which may have bad or biased content.

Therefore, SFT is not the end of NLP field, but rather a useful and strong way that can make language models do more things and allow new uses. However, there is still a lot of space for improvement and creativity in NLP research and work.
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