发表机构
University of Houston; The University of Sydney（休斯顿大学; 悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出黑盒提示词最小化框架，将少样本提示词缩减至必要子集，平均减少65.3%字符数且保持输出保真，并揭示模型作为通用编码器或解码器的角色。
AI 中文摘要
提示词是引导大型语言模型（LLM）行为的主要机制。然而，提示词的内部结构和因果层级仍未被充分理解：哪些部分在因果上是必要的，哪些是冗余的，这是一个开放问题。这种不透明性可能带来严重后果。细微的提示词变化可能在关键软件系统中悄然改变模型输出，而工程师缺乏推理提示词可靠性的技术。我们提出了\ ramework，一个黑盒提示词最小化框架，可将少样本提示词缩减至其必要的最小子集。我们通过一个案例研究将\ ramework应用于少样本学习系统，并展示了该框架所能提供的洞见。我们的实验表明，少样本示例的字符数平均可减少65.3%±15.8%，同时完全保持命题输出保真度。模型优先保留逻辑标识符和约束声明，而丢弃自然语言散文和跨提示词关系注释。我们的分析还表明，一些模型是通用编码器，能够生成高度清晰且最小化的提示词，而另一些模型则是通用解码器，能够解释来自大多数其他模型的最小化提示词。通过识别哪些组件是不可或缺的，\ ramework为提示词压缩和少样本示例的结构分析提供了原则性基础。
英文摘要
Prompts are the primary mechanism for directing the behavior of large language models (LLMs). Yet the internal structure and causal hierarchy of prompts remain poorly understood: which parts are causally necessary and which are redundant is an open question. This opacity can have severe consequences. Subtle prompt variations can silently shift model outputs in critical software systems, and engineers lack techniques to reason about prompt reliability. We present \framework, a blackbox prompt-minimization framework that reduces few-shot prompts to their necessary minimal subset. We use a case study to apply \framework to a few-shot learning system and demonstrate the insights that this framework can provide. Our experiments show that few-shot exemplars can be reduced by a mean of 65.3\%~$\pm$~15.8\% in character count while fully preserving propositional output fidelity. The models preferentially retain logical identifiers and constraint declarations while discarding natural language prose and cross-prompt relational annotations. Our analysis also shows that some models are universal encoders, able to produce highly legible yet minimized prompts, while others are universal decoders, able to interpret minimized prompts from most other models. By identifying which components are indispensable, \framework provides a principled basis for prompt compression and structural analysis of few-shot exemplars.
