It remains an open question whether incorporating external knowledge benefits
commonsense reasoning while maintaining the flexibility of pretrained sequence
models. To investigate this question, we develop generated knowledge prompting,
which consists of generating knowledge from a language model, then providing
the knowledge as additional input when answering a question. Our method does
not require task-specific supervision for knowledge integration, or access to a
structured knowledge base, yet it improves performance of large-scale,
state-of-the-art models on four commonsense reasoning tasks, achieving
state-of-the-art results on numerical commonsense (NumerSense), general
commonsense (CommonsenseQA 2.0), and scientific commonsense (QASC) benchmarks.
Generated knowledge prompting highlights large-scale language models as
flexible sources of external knowledge for improving commonsense reasoning. Our
code is available at this https URL

通过从语言模型中产生知识并将其作为额外输入用于回答问题，我们开发了一种生成知识提示方法，它不需要针对知识集成的任务特定监督，也不需要访问结构化的知识库，并在四个常识推理任务中提高了大规模、最先进模型的性能。这凸显了大规模语言模型作为提高常识推理的外部知识的灵活来源。