  Large Language Models (LLMs) are capable of performing zero-shot closed-book
question answering tasks, based on their internal knowledge stored in
parameters during pre-training. However, such internalized knowledge might be
insufficient and incorrect, which could lead LLMs to generate factually wrong
answers. Furthermore, fine-tuning LLMs to update their knowledge is expensive.
To this end, we propose to augment the knowledge directly in the input of LLMs.
Specifically, we first retrieve the relevant facts to the input question from
the knowledge graph based on semantic similarities between the question and its
associated facts. After that, we prepend the retrieved facts to the input
question in the form of the prompt, which is then forwarded to LLMs to generate
the answer. Our framework, Knowledge-Augmented language model PromptING
(KAPING), requires no model training, thus completely zero-shot. We validate
the performance of our KAPING framework on the knowledge graph question
answering task, that aims to answer the user's question based on facts over a
knowledge graph, on which ours outperforms relevant zero-shot baselines by up
to 48% in average, across multiple LLMs of various sizes.
