  In this paper, we demonstrate how Large Language Models (LLMs) can
effectively learn to use an off-the-shelf information retrieval (IR) system
specifically when additional context is required to answer a given question.
Given the performance of IR systems, the optimal strategy for question
answering does not always entail external information retrieval; rather, it
often involves leveraging the parametric memory of the LLM itself. Prior
research has identified this phenomenon in the PopQA dataset, wherein the most
popular questions are effectively addressed using the LLM's parametric memory,
while less popular ones require IR system usage. Following this, we propose a
tailored training approach for LLMs, leveraging existing open-domain question
answering datasets. Here, LLMs are trained to generate a special token, <RET>,
when they do not know the answer to a question. Our evaluation of the Adaptive
Retrieval LLM (Adapt-LLM) on the PopQA dataset showcases improvements over the
same LLM under three configurations: (i) retrieving information for all the
questions, (ii) using always the parametric memory of the LLM, and (iii) using
a popularity threshold to decide when to use a retriever. Through our analysis,
we demonstrate that Adapt-LLM is able to generate the <RET> token when it
determines that it does not know how to answer a question, indicating the need
for IR, while it achieves notably high accuracy levels when it chooses to rely
only on its parametric memory.
