PIAST：基于上下文增强的快速提示生成以应对稀少训练数据
PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data
AI总结：
PIAST通过生成少量示例快速构建提示，利用蒙特卡洛沙普利估计优化示例效用，实现在有限计算资源下提升文本简化、GSM8K和分类任务的性能。
摘要：
大型语言模型对提示设计高度敏感，但手动构建有效提示困难且需复杂设计。本文提出一种快速自动提示构建算法，通过生成少量示例增强人类指令。方法通过迭代替换/删除/保留示例，利用蒙特卡洛沙普利估计评估示例效用。为加快执行，采用激进的子采样和回放缓冲区。该方法可在不同计算预算下运行。在有限预算下，PIAST在文本简化和GSM8K任务上优于现有自动提示方法，在分类和总结任务上取得第二名。扩展但仍适度的计算预算下，PIAST在分类、简化和GSM8K任务上达到新的状态。结果表明，精心构建的示例而非全面的指令搜索是快速且数据高效的提示工程关键。代码可在https://github.com/Batorskq/PIAST获取。
英文摘要：
LLMs are highly sensitive to prompt design, but handcrafting effective prompts is difficult and often requires intricate crafting of few-shot examples. We propose a fast automatic prompt construction algorithm that augments human instructions by generating a small set of few shot examples. Our method iteratively replaces/drops/keeps few-shot examples using Monte Carlo Shapley estimation of example utility. For faster execution, we use aggressive subsampling and a replay buffer for faster evaluations. Our method can be run using different compute time budgets. On a limited budget, we outperform existing automatic prompting methods on text simplification and GSM8K and obtain second best results on classification and summarization. With an extended, but still modest compute budget we set a new state of the art among automatic prompting methods on classification, simplification and GSM8K. Our results show that carefully constructed examples, rather than exhaustive instruction search, are the dominant lever for fast and data efficient prompt engineering. Our code is available at https://github.com/Batorskq/PIAST.
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。
