  Retrieval Augmented Generation (RAG) enriches the ability of language models
to reason using external context to augment responses for a given user prompt.
This approach has risen in popularity due to practical applications in various
applications of language models in search, question/answering, and chat-bots.
However, the exact nature of how this approach works isn't clearly understood.
In this paper, we mechanistically examine the RAG pipeline to highlight that
language models take shortcut and have a strong bias towards utilizing only the
context information to answer the question, while relying minimally on their
parametric memory. We probe this mechanistic behavior in language models with:
(i) Causal Mediation Analysis to show that the parametric memory is minimally
utilized when answering a question and (ii) Attention Contributions and
Knockouts to show that the last token residual stream do not get enriched from
the subject token in the question, but gets enriched from other informative
tokens in the context. We find this pronounced shortcut behaviour true across
both LLaMa and Phi family of models.
