用合成数据自动优化提示词，让大模型回答更准。
SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback
- 构建闭环系统：生成合成数据暴露提示缺陷，再优化提示。
- 在多个问答和推理任务中，效果优于传统提示调优方法。
- 无需外部标注或新任务，适合持续改进的场景。
提示词质量对大语言模型性能至关重要，促使大量研究聚焦提示优化。现有方法通常在固定数据集上优化提示，假设输入分布不变，且难以支持迭代改进。本文提出SIPDO（通过数据增强优化实现自提升提示），一种将合成数据生成融入优化过程的闭环框架。SIPDO将合成数据生成器与提示优化器耦合，生成揭示当前提示弱点的新样本，优化器据此逐步改进提示。该反馈驱动循环可在无外部监督或新任务的前提下，系统性提升提示性能。在问答和推理基准上的实验表明，SIPDO显著优于标准提示调优方法，凸显了将数据合成融入提示学习流程的价值。
原文摘要 · Abstract (English)
Prompt quality plays a critical role in the performance of large language models (LLMs), motivating a growing body of work on prompt optimization. Most existing methods optimize prompts over a fixed dataset, assuming static input distributions and offering limited support for iterative improvement. We introduce SIPDO (Self-Improving Prompts through Data-Augmented Optimization), a closed-loop framework for prompt learning that integrates synthetic data generation into the optimization process. SIPDO couples a synthetic data generator with a prompt optimizer, where the generator produces new examples that reveal current prompt weaknesses and the optimizer incrementally refines the prompt in response. This feedback-driven loop enables systematic improvement of prompt performance without assuming access to external supervision or new tasks. Experiments across question answering and reasoning benchmarks show that SIPDO outperforms standard prompt tuning methods, highlighting the value of integrating data synthesis into prompt learning workflows.
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