Abstract
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.
Abstract (translated)
在本文中，我们证明了大型语言模型（LLMs）在需要额外上下文来回答给定问题时，可以有效地学习使用标准的信息检索（IR）系统。考虑到IR系统的性能，问题回答的最佳策略并不总是涉及外部信息检索，而是通常利用LLM本身的参数化记忆。之前的研究已经发现了这个现象在PopQA数据集中，其中最流行的问题有效地使用LLM的参数化记忆来回答，而较不流行的问题则需要使用IR系统。接着，我们为LLMs提出了一个针对现有开放领域问题回答数据集的定制化训练方法。在这里，LLMs在不知道答案时生成一个特殊标记<RET>。我们对PopQA数据集上的自适应检索LLM（Adapt-LLM）的评估展示了在三种配置下的改进：（i）检索所有问题，（ii）始终使用LLM的参数化记忆，（iii）根据流行度阈值来决定何时使用检索器。通过我们的分析，我们证明了Adapt-LLM能够生成<RET>标记，当它确定自己无法回答问题时，表明需要IR，而当它仅依赖参数化记忆时，其准确率显著提高。
URL
https://arxiv.org/abs/2404.19705
