This paper presents null-shot prompting. Null-shot prompting exploits
hallucination in large language models (LLMs) by instructing LLMs to utilize
information from the "Examples" section that never exists within the provided
context to perform a task. While reducing hallucination is crucial and
non-negligible for daily and critical uses of LLMs, we propose that in the
current landscape in which these LLMs still hallucinate, it is possible, in
fact, to exploit hallucination to increase performance in performing tasks
compared to standard zero-shot prompting. Experiments with six LLMs show
improvements in performance across the majority of eight datasets, including
reading comprehension, arithmetic reasoning, and closed-book question
answering. The observed inconsistency in increased relative performance across
LLMs also potentially indicates a different degree of inherent hallucination in
each model. These differences show that it is possible to utilize null-shot
prompting as a way to detect degrees of hallucination in LLMs using existing
benchmarking datasets. We also perform ablation studies, including
experimenting with a modified version of null-shot prompting that incorporates
ideas from zero-shot chain-of-thought prompting, which shows different trends
of results.

通过使用不存在于上下文提供的信息指导大型语言模型利用幻觉来执行任务，在大多数数据集中提高性能，并检测存在于不同模型的幻觉程度。