Abstract
Large Language Models (LLMs) have improved substantially alignment, yet their behavior remains highly sensitive to prompt phrasing. This brittleness has motivated automated prompt engineering, but most existing methods (i) require a task-specific training set, (ii) rely on expensive iterative optimization to produce a single dataset-level prompt, and (iii) must be rerun from scratch for each new task. We introduce TATRA, a dataset-free prompting method that constructs instance-specific few-shot prompts by synthesizing on-the-fly examples to accompany a user-provided instruction. TATRA requires no labeled training data and avoids task-specific optimization loops, while retaining the benefits of demonstration-based prompting. Across standard text classification benchmarks, TATRA matches or improves over strong prompt-optimization baselines that depend on training data and extensive search. On mathematical reasoning benchmarks, TATRA achieves state-of-the-art performance on GSM8K and DeepMath, outperforming methods that explicitly optimize prompts on those tasks. Our results suggest that per-instance construction of effective in-context examples is more important than running long, expensive optimization loops to produce a single prompt per task. We will make all code publicly available upon acceptance of the paper. Code is available at this https URL
Abstract (translated)
大型语言模型（LLMs）在对齐方面有了显著的改进，但它们的行为仍然高度依赖于提示语句的表述。这种脆弱性促使了自动化提示工程的发展，然而大多数现有的方法需要特定任务的训练集、依靠昂贵的迭代优化来生成单一的数据级提示，并且每次遇到新的任务时都需要从头开始重新运行。 我们引入了一种名为TATRA的方法，这是一种不依赖于数据集的提示技术，它通过实时合成示例以伴随用户提供的指令来构建针对每个实例的少量样本提示。TATRA不需要标注的训练数据并且避免了特定任务的优化循环，同时保留了基于演示的提示方法的优点。 在标准文本分类基准测试中，与依赖于训练数据和大量搜索的强大提示优化基线相比，TATRA能够达到或超越它们的表现水平。在数学推理基准上，TATRA在GSM8K和DeepMath任务上实现了最先进的性能，超过了那些专门在这类任务上进行提示优化的方法。 我们的研究结果表明，针对每个实例构建有效的上下文示例比为单一的任务运行漫长且昂贵的优化循环更为重要。一旦论文被接受，我们将公开所有代码。代码可以在提供的链接处获取（假设原文中的链接可以正常使用）。
URL
https://arxiv.org/abs/2603.03298
