  Retrieval Augmented Generation (RAG) represents a significant advancement in
artificial intelligence combining a retrieval phase with a generative phase,
with the latter typically being powered by large language models (LLMs). The
current common practices in RAG involve using "instructed" LLMs, which are
fine-tuned with supervised training to enhance their ability to follow
instructions and are aligned with human preferences using state-of-the-art
techniques. Contrary to popular belief, our study demonstrates that base models
outperform their instructed counterparts in RAG tasks by 20% on average under
our experimental settings. This finding challenges the prevailing assumptions
about the superiority of instructed LLMs in RAG applications. Further
investigations reveal a more nuanced situation, questioning fundamental aspects
of RAG and suggesting the need for broader discussions on the topic; or, as
Fromm would have it, "Seldom is a glance at the statistics enough to understand
the meaning of the figures".
