让提示词主动补知识，比光靠提问更懂专业领域。
Beyond Elicitation: Provision-based Prompt Optimization for Knowledge-Intensive Tasks
- 用系统性补知识替代单纯找最优提问，提升模型理解力。
- 在15个任务上平均性能提升6%，且推理耗能降低29%。
- 适合需要精准知识的科研、医疗等专业场景使用。
尽管提示词优化已成为提升语言模型性能的关键技术，现有方法多聚焦于通过寻找最佳提示来激发模型能力，但在知识密集型任务中存在根本局限：它们仅在静态知识容量内操作，无法提供专业领域所需的事实知识、术语精度和推理模式。为此，我们提出基于知识供给的提示优化框架（KPPO），将提示优化重构为系统性知识融合过程。KPPO引入三项创新：1）知识缺口识别与针对性修复机制；2）兼顾性能提升与分布稳定性的批量候选评估方法；3）自适应知识剪枝策略，在保持性能的同时减少高达29%的推理令牌消耗。在涵盖多个领域的15个知识密集型基准测试中，KPPO显著优于基于激发的方法，平均性能提升约6%，且推理成本相当或更低。
原文摘要 · Abstract (English)
While prompt optimization has emerged as a critical technique for enhancing language model performance, existing approaches primarily focus on elicitation-based strategies that search for optimal prompts to activate models' capabilities. These methods exhibit fundamental limitations when addressing knowledge-intensive tasks, as they operate within static knowledge capacity rather than providing the factual knowledge, terminology precision, and reasoning patterns required in specialized domains. To address these limitations, we propose Knowledge-Provision-based Prompt Optimization (KPPO), a framework that reformulates prompt optimization as systematic knowledge integration rather than potential elicitation. KPPO introduces three key innovations: 1) a knowledge gap filling mechanism for knowledge gap identification and targeted remediation; 2) a batch-wise candidate evaluation approach that considers both performance improvement and distributional stability; 3) an adaptive knowledge pruning strategy that balances performance and token efficiency, reducing up to 29% of inference token usage. Evaluation on 15 knowledge-intensive benchmarks from various domains demonstrates KPPO's superiority over elicitation-based methods, with an average improvement of ~6% over baselines while achieving comparable or lower token consumption.
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