ZERA用原则化方法从零开始优化提示，少样本快速生成高质量指令。
ZERA: Zero-init Instruction Evolving Refinement Agent -- From Zero Instructions to Structured Prompts via Principle-based Optimization
- 基于八项通用标准自动评分并迭代优化系统与用户提示。
- 仅需少量样本和短周期迭代，五模型九数据集均显著提升性能。
- 适合需要高效构建精准提示的开发者与研究者使用。
自动提示优化（APO）通过改进特定任务的提示来提升大语言模型（LLM）表现。然而，现有APO方法通常仅关注用户提示，依赖非结构化反馈，且需大量样本与长时间迭代，成本高且脆弱。本文提出ZERA（Zero-init Instruction Evolving Refinement Agent），一种通过原则性、低开销方式联合优化系统与用户提示的新框架。ZERA利用八项可泛化的评价标准自动推断权重对提示进行评分，并依据结构化批评进行修正，实现以极小样本和短迭代周期快速收敛至高质量提示。我们在五种大语言模型和九个涵盖推理、摘要、代码生成任务的多样化数据集上评估ZERA，实验结果表明其持续优于强基线。消融研究进一步验证了各组件对有效提示构建的贡献。所有提示实现已公开于 https://github.com/younatics/zera-agent。
原文摘要 · Abstract (English)
Automatic Prompt Optimization (APO) improves large language model (LLM) performance by refining prompts for specific tasks. However, prior APO methods typically focus only on user prompts, rely on unstructured feedback, and require large sample sizes and long iteration cycles-making them costly and brittle. We propose ZERA (Zero-init Instruction Evolving Refinement Agent), a novel framework that jointly optimizes both system and user prompts through principled, low-overhead refinement. ZERA scores prompts using eight generalizable criteria with automatically inferred weights, and revises prompts based on these structured critiques. This enables fast convergence to high-quality prompts using minimal examples and short iteration cycles. We evaluate ZERA across five LLMs and nine diverse datasets spanning reasoning, summarization, and code generation tasks. Experimental results demonstrate consistent improvements over strong baselines. Further ablation studies highlight the contribution of each component to more effective prompt construction. Our implementation including all prompts is publicly available at https://github.com/younatics/zera-agent.
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