Open-Domain Question Answering (ODQA) requires models to answer factoid
questions with no context given. The common way for this task is to train
models on a large-scale annotated dataset to retrieve related documents and
generate answers based on these documents. In this paper, we show that the ODQA
architecture can be dramatically simplified by treating Large Language Models
(LLMs) as a knowledge corpus and propose a Self-Prompting framework for LLMs to
perform ODQA so as to eliminate the need for training data and external
knowledge corpus. Concretely, we firstly generate multiple pseudo QA pairs with
background passages and one-sentence explanations for these QAs by prompting
LLMs step by step and then leverage the generated QA pairs for in-context
learning. Experimental results show our method surpasses previous
state-of-the-art methods by +8.8 EM averagely on three widely-used ODQA
datasets, and even achieves comparable performance with several
retrieval-augmented fine-tuned models.

本文提出了一种基于大型语言模型的自我点拨框架 (Self-Prompting framework)，使得在开放域下的问答任务 (Open-Domain Question Answering) 可以在不需要训练数据和外部知识库的情况下实现，采用该方法在三个广泛使用的 ODQA 数据集上，实验结果优于之前的最先进方法，在 EM 指标上平均提高了 8.8 个百分点，并且能够实现与多种检索增强的微调模型相比较的性能。