大型语言模型可能并不在意你说了什么：提示格式胜过描述
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
AI总结：
本文提出集成提示框架，发现其性能提升主要源于格式而非描述内容，并验证了在多种任务上设计良好提示格式比具体描述更有效。
摘要：
借助上下文学习（ICL），大型语言模型（LLMs）已在各种任务上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。在本工作中，我们提出了一种集成提示框架，用于描述多个上下文示例的选择标准，并在涵盖六个翻译方向的机器翻译（MT）初步实验中确认，该框架提升了ICL性能。但令我们惊讶的是，LLMs可能并不真正在意描述的实际内容，性能提升主要源于集成格式，因为即使使用随机的描述性名词，该格式也能带来改进。我们进一步将这一新集成框架应用于涵盖常识、数学、逻辑推理和幻觉任务的多种任务上，使用三个LLMs并取得了有前景的结果，这再次表明，设计合适的提示格式比在具体描述上投入精力更为有效和高效。
英文摘要：
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 performance. But to our surprise, LLMs might not care what the descriptions actually say, and the performance gain is primarily caused by the ensemble format, since it could lead to improvement even with random descriptive nouns. We further apply this new ensemble framework 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
