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Fwiw, unpublished testing on LLaMA-1 13B showed that it was able to learn a new word and it's meaning via PEFT with <50 examples. Finetuning can unquestionably add new data to a model.

Jeremy Howard has written a bit about how quickly LLMs can pick up new concepts as well:

https://www.fast.ai/posts/2023-09-04-learning-jumps/

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The question of how to fine-tune to teach LLMs facts/knowledge is definitely something we're interested in exploring more in future work. The common opinion seems to me at least to be that fine-tuning is more to teach the model how to use the knowledge it already has to complete a specific task rather than to instill new knowledge, and that RAG should be sued to provide more specific context. However, I personally believe there is potential in fine-tuning for "memorization" or learning, and am excited to see new developments in the field.

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