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Enabling large language models (LLMs) to perform tasks in zero-shot has beenan appealing goal owing to its labor-saving (i.e., requiring no task-specificannotations); as such, zero-shot prompting approaches also enjoy better taskgeneralizability. To improve LLMs' zero-shot performance, prior work hasfocused on devising more effective task instructions (e.g., ``let's think stepby step'' ). However, we argue that, in order for an LLM to solve themcorrectly in zero-shot, individual test instances need more carefully designedand customized instructions. To this end, we propose PRoMPTd, an approach thatrewrites 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. Weevaluated PRoMPTd on eight datasets covering tasks including arithmetics,logical reasoning, and code generation, using GPT-4 as the task LLM. Notably,PRoMPTd achieves an absolute improvement of around 10% on the complex MATHdataset and 5% on the code generation task on HumanEval, outperformingconventional zero-shot methods. In addition, we also showed that the rewrittenprompt can provide better interpretability of how the LLM resolves each testinstance, which can potentially be leveraged as a defense mechanism againstadversarial prompting. The source code and dataset can be obtained fromhttps://github.com/salokr/PRoMPTd

Instance Needs More Care: Rewriting Prompts for Instances Yields Better Zero-Shot Performance