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Large language models (LLMs) have revolutionized zero-shot task performance,mitigating the need for task-specific annotations while enhancing taskgeneralizability. Despite its advancements, current methods using triggerphrases such as "Let's think step by step" remain limited. This studyintroduces PRomPTed, an approach that optimizes the zero-shot prompts forindividual task instances following an innovative manner of "LLMs in the loop".Our comprehensive evaluation across 13 datasets and 10 task types based onGPT-4 reveals that PRomPTed significantly outperforms both the naive zero-shotapproaches and a strong baseline (i.e., "Output Refinement") which refines thetask output instead of the input prompt. Our experimental results alsoconfirmed the generalization of this advantage to the relatively weakerGPT-3.5. Even more intriguingly, we found that leveraging GPT-3.5 to rewriteprompts for the stronger GPT-4 not only matches but occasionally exceeds theefficacy of using GPT-4 as the prompt rewriter. Our research thus presents ahuge value in not only enhancing zero-shot LLM performance but also potentiallyenabling supervising LLMs with their weaker counterparts, a capabilityattracting much interest recently. Finally, our additional experiments confirmthe generalization of the advantages to open-source LLMs such as Mistral 7B andMixtral 8x7B.

Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance