This is a common misunderstanding; fine-tuning a model does not mean teaching the model new information. Fine-tuning is used to adapt the model to perform better in a specific task or domain using the information it already has, in a specialized way (like a chatbot for question/answer interactions). Training a LLM on new data is extremely expensive so it's not possible.
Ok then. So how to teach the LLM new information/jargon ? I repeat: I tried to do this with GPT (fine-tuning and embeddings) and I got no improvements on jargon and re-training is out of reach, of course.
