让提示词同时优化多个目标，提升大模型表现。
MODP: Multi Objective Directional Prompting
- 从模型内在行为出发，多目标优化提示词设计。
- 在摘要任务上比初始提示提升26%性能。
- 已落地企业支持系统，服务超万级员工与百万客户。
大语言模型（LLMs）应用广泛，但提示词工程仍依赖经验且主观。现有研究多聚焦特定任务优化，忽视模型自身行为。本文提出MODP——多目标定向提示框架，核心包含两点：1）多目标性：将模型内在行为作为提示设计的额外目标；2）定向提示：基于指标驱动的方法，确保生成稳健高精度提示。我们在合成数据集上的摘要任务中验证了该方法，相比初始提示性能提升26%。最后，将MODP应用于戴尔公司下一代最佳行动支持工具，现已投入生产，服务于超过10,000名内部支持人员，并覆盖全球数百万客户。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have led to their popularity across multiple use-cases. However, prompt engineering, the process for optimally utilizing such models, remains approximation-driven and subjective. Most of the current research on prompt engineering focuses on task-specific optimization, while neglecting the behavior of the LLM under consideration during prompt development. This paper introduces MODP -- Multi Objective Directional Prompting, a framework based on two key concepts: 1) multi-objectivity: the importance of considering an LLM's intrinsic behavior as an additional objective in prompt development, and 2) directional prompting: a metrics-driven method for prompt engineering to ensure development of robust and high-precision prompts. We demonstrate the effectiveness of our proposed ideas on a summarization task, using a synthetically created dataset, achieving a 26% performance gain over initial prompts. Finally, we apply MODP to develop prompts for Dell's Next Best Action support tool, which is now in production and is used by more than 10,000 internal support agents and serving millions of customers worldwide.
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