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.

本文介绍了如何使用大型语言模型（LLMs）有效地学习使用现成的信息检索系统来回答问题时所需的附加上下文。通过在 PopQA 数据集上进行评估，我们展示了 Adapt-LLM 在使用所有问题的信息检索、仅使用 LLM 的参数存储器以及使用人气阈值来决定何时使用检索程序三种配置下，相比相同 LLM 的改进。通过我们的分析，我们证明了当 Adapt-LLM 确定无法回答问题时，它能生成 <RET> 标记，表明需要进行信息检索，同时当只依赖参数存储器时，其达到明显高的准确率水平。