Knowledge-intensive tasks, such as open-domain question answering (QA),
require access to a large amount of world or domain knowledge. A common
approach for knowledge-intensive tasks is to employ a retrieve-then-read
pipeline that first retrieves a handful of relevant contextual documents from
an external corpus such as Wikipedia and then predicts an answer conditioned on
the retrieved documents. In this paper, we present a novel perspective for
solving knowledge-intensive tasks by replacing document retrievers with large
language model generators. We call our method generate-then-read (GenRead),
which first prompts a large language model to generate contextutal documents
based on a given question, and then reads the generated documents to produce
the final answer. Furthermore, we propose a novel clustering-based prompting
method that selects distinct prompts, resulting in the generated documents that
cover different perspectives, leading to better recall over acceptable answers.
We conduct extensive experiments on three different knowledge-intensive tasks,
including open-domain QA, fact checking, and dialogue system. Notably, GenRead
achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly
outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0
and +3.9, without retrieving any documents from any external knowledge source.
Lastly, we demonstrate the model performance can be further improved by
combining retrieval and generation. Our code and generated documents can be
found at this https URL.

本文提出了一种新的方法 GenRead，它使用大型语言模型生成上下文文档来解决开放领域 QA 等知识密集型任务，并且借助基于聚类的提示方法来选择不同的提示从而提高回收率，实验证明相比于传统的检索 - 阅读模型，这种方法有更高的准确性。