Multi-step LLM pipelines invoke large language models multiple times in a structured sequence and can effectively solve complex tasks, but their performance heavily depends on the prompts used at each step. Jointly optimizing these prompts is difficult due to missing step-level supervision and inter-step dependencies. Existing end-to-end prompt optimization methods struggle under these conditions and often yield suboptimal or unstable updates. We propose ADOPT, an Adaptive Dependency-aware Prompt Optimization framework for multi-step LLM pipelines. ADOPT explicitly models the dependency between each LLM step and the final task outcome, enabling precise text-gradient estimation analogous to computing analytical derivatives. It decouples textual gradient estimation from gradient updates, reducing multi-prompt optimization to flexible single-prompt optimization steps, and employs a Shapley-based mechanism to adaptively allocate optimization resources. Experiments on real-world datasets and diverse pipeline structures show that ADOPT is effective and robust, consistently outperforming state-of-the-art prompt optimization baselines.

翻译：多步LLM流程通过结构化序列多次调用大语言模型，能有效解决复杂任务，但其性能高度依赖于各步骤使用的提示。由于缺乏步骤级监督和步骤间依赖关系，联合优化这些提示具有挑战性。现有的端到端提示优化方法在此类条件下表现不佳，常产生次优或不稳定的更新。我们提出ADOPT——一种面向多步LLM流程的自适应依赖感知提示优化框架。ADOPT显式建模每个LLM步骤与最终任务结果之间的依赖关系，实现类似于解析导数计算的精确文本梯度估计。该方法将文本梯度估计与梯度更新解耦，将多提示优化问题简化为灵活的单提示优化步骤，并采用基于Shapley值的机制自适应分配优化资源。在真实数据集和多样化流程结构上的实验表明，ADOPT具有高效性和鲁棒性，其性能持续优于最先进的提示优化基线方法。