COSTAR-A增强小模型问答输出结构，提升指令响应准确性
COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions
- 在COSTAR基础上增加答案模板，引导更明确的输出结构
- 80亿参数以下小模型使用后输出更清晰、决策更果断
- 适合资源受限设备部署，尤其对本地化模型有效
大型语言模型对提示设计极为敏感，优化提示是生成一致高质量输出的关键。本文提出COSTAR-A，是在原有COSTAR框架（上下文、目标、风格、语气、受众、回应）基础上，末尾新增'答案'组件的提示工程框架。实验表明，尽管原始COSTAR能提升大模型输出一致性，但在小型本地化模型上表现不稳，尤其在需强指令或约束输出的任务中。通过针对最大80亿参数的微调模型进行多轮受控评估发现，COSTAR-A可显著改善小模型输出结构与决断力，其效果因模型和任务而异。值得注意的是，Llama 3.1-8B模型在使用COSTAR-A时相较仅用COSTAR有性能提升。结果表明，COSTAR-A具备良好适应性与可扩展性，特别适用于计算资源受限环境下的高效AI部署。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are highly sensitive to prompt design, and making optimized prompting techniques is crucial for generating consistent, high-quality outputs. In this study, we introduce COSTAR-A, a novel prompt engineering framework that enhances the existing COSTAR method, which stands for Context, Objective, Style, Tone, Audience, and Response, by adding the 'Answer' component at the end. We demonstrate that while the original COSTAR framework improves prompt clarity and aligns outputs for larger LLMs, its performance is less consistent with smaller, locally optimized models, particularly in tasks that require more directive or constrained outputs. Through a series of controlled prompt-output assessments with smaller (at most 8 billion parameters), fine-tuned models, we found that COSTAR-A can enhance the output structure and decisiveness of localized LLMs for certain tasks, although its effectiveness varies across models and use cases. Notably, the Llama 3.1-8B model exhibited performance improvements when prompted with COSTAR-A compared to COSTAR alone. These findings emphasize the adaptability and scalability of COSTAR-A as a prompting framework, particularly in computationally efficient AI deployments on resource-constrained hardware.
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