Enabling large language models (LLMs) to perform tasks in zero-shot has been
an appealing goal owing to its labor-saving (i.e., requiring no task-specific
annotations); as such, zero-shot prompting approaches also enjoy better task
generalizability. To improve LLMs' zero-shot performance, prior work has
focused on devising more effective task instructions (e.g., ``let's think step
by step'' ). However, we argue that, in order for an LLM to solve them
correctly in zero-shot, individual test instances need more carefully designed
and customized instructions. To this end, we propose PRoMPTd, an approach that
rewrites the task prompt for each individual test input to be more specific,
unambiguous, and complete, so as to provide better guidance to the task LLM. We
evaluated PRoMPTd on eight datasets covering tasks including arithmetics,
logical reasoning, and code generation, using GPT-4 as the task LLM. Notably,
\algoname achieves an absolute improvement of around 10\% on the complex MATH
dataset and 5\% on the code generation task on HumanEval, outperforming
conventional zero-shot methods. In addition, we also showed that the rewritten
prompt can provide better interpretability of how the LLM resolves each test
instance, which can potentially be leveraged as a defense mechanism against
adversarial prompting. The source code and dataset can be obtained from
this https URL

为了提高大型语言模型的零样本性能，我们提出了 PRoMPTd 方法，通过为每个测试输入重写任务提示来实现更具体、明确和完整的指导，实现了对不同任务的更好解决方案，并提供了更好的解释性以应对对抗性提示。