  Large language models (LLMs) encapsulate a vast amount of factual information
within their pre-trained weights, as evidenced by their ability to answer
diverse questions across different domains. However, this knowledge is
inherently limited, relying heavily on the characteristics of the training
data. Consequently, using external datasets to incorporate new information or
refine the capabilities of LLMs on previously seen information poses a
significant challenge. In this study, we compare two common approaches:
unsupervised fine-tuning and retrieval-augmented generation (RAG). We evaluate
both approaches on a variety of knowledge-intensive tasks across different
topics. Our findings reveal that while unsupervised fine-tuning offers some
improvement, RAG consistently outperforms it, both for existing knowledge
encountered during training and entirely new knowledge. Moreover, we find that
LLMs struggle to learn new factual information through unsupervised
fine-tuning, and that exposing them to numerous variations of the same fact
during training could alleviate this problem.
