With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instructions during ICL remains under-explored. In this work, we propose an ensemble prompt framework to describe the selection criteria of multiple in-context examples, and preliminary experiments on machine translation (MT) across six translation directions confirm that this framework boosts ICL perfromance. But to our surprise, LLMs might not necessarily care what the descriptions actually say, and the performance gain is primarily caused by the ensemble format, since the framework could lead to improvement even with random descriptive nouns. We further apply this new ensemble prompt on a range of commonsense, math, logical reasoning and hallucination tasks with three LLMs and achieve promising results, suggesting again that designing a proper prompt format would be much more effective and efficient than paying effort into specific descriptions. Our code will be publicly available once this paper is published.

翻译：借助上下文学习（ICL），大型语言模型（LLM）在各种任务中取得了令人瞩目的性能。然而，描述性指令在ICL过程中的作用仍未得到充分探索。本研究提出一种集成提示框架，用于描述多个上下文示例的选择标准。在六个翻译方向上的机器翻译（MT）初步实验证实，该框架能有效提升ICL性能。但令人意外的是，LLM可能并不真正关注描述的具体内容，性能提升主要源于集成格式本身——因为即使使用随机描述性名词，该框架仍能带来改进效果。我们进一步将这种新型集成提示应用于常识推理、数学计算、逻辑推理和幻觉检测等多项任务，并在三种LLM上取得了显著效果，这再次表明：设计恰当的提示格式远比投入精力构思具体描述内容更为有效和高效。本文发表后，相关代码将公开提供。