Learning from Guidelines: Structured Prompt Optimization for Expert Annotation Tasks

Deep learning has significantly advanced numerous fields by training on extensive annotated datasets. However, this data-driven paradigm faces limitations such as limited adaptability and high annotation costs, particularly when precise adherence to detailed, domain-specific guidelines is required in annotation. This challenge raises a critical question: Can models effectively shift from data-driven learning to autonomously leveraging guidelines with minimal annotated examples? To address this, we propose the Guideline-Driven Prompt (GDP) optimization framework, which shifts the learning paradigm from data-driven training to guideline-driven reasoning. GDP leverages Retrieval Augmented Generation (RAG) to retrieve essential fragments from complex guidelines and synthesize them into structured, executable prompts. A tree-based optimization algorithm systematically constructs and refines these prompts, explicitly capturing the intricate logic embedded in professional guidelines through a latent pipeline structure. Empirical evaluations on four datasets ranging from diverse domains and different tasks demonstrate that GDP effectively transitions the learning process from data-intensive methods to a guideline-driven approach in tasks requiring detailed and complex guideline adherence, reducing dependence on extensive annotated datasets.

Authors

• Wenliang Zhong
• Haiqing Li
• Thao M. Dang University of Texas at Arlington
• Feng Jiang The Chinese University of Hong Kong, Shenzhen Shenzhen Research Institute of Big Data University of Science and Technology of China
• Hehuan Ma University of Texas at Arlington
• Yuzhi Guo University of Texas at Arlington
• Jean Gao University of Texas at Arlington
• Junzhou Huang University of Texas at Arlington

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