Zero-shot Dialog State Tracking (zs-DST) is essential for enabling Task-Oriented Dialog Systems (TODs) to generalize to new domains without costly data annotation. A central challenge lies in the semantic misalignment between dynamic dialog contexts and static prompts, leading to inflexible cross-layer coordination, domain interference, and catastrophic forgetting. To tackle this, we propose Hierarchical Collaborative Low-Rank Adaptation (HiCoLoRA), a framework that enhances zero-shot slot inference through robust prompt alignment. It features a hierarchical LoRA architecture for dynamic layer-specific processing (combining lower-layer heuristic grouping and higher-layer full interaction), integrates Spectral Joint Domain-Slot Clustering to identify transferable associations (feeding an Adaptive Linear Fusion Mechanism), and employs Semantic-Enhanced SVD Initialization (SemSVD-Init) to preserve pre-trained knowledge. Experiments on multi-domain datasets MultiWOZ and SGD show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST. Code is available at https://github.com/carsonz/HiCoLoRA.

翻译：零样本对话状态跟踪（zs-DST）对于任务型对话系统（TODs）无需昂贵数据标注即可泛化至新领域至关重要。其核心挑战在于动态对话上下文与静态提示之间的语义错位，这会导致跨层协调僵化、领域干扰及灾难性遗忘。为此，我们提出分层协同低秩适应（HiCoLoRA）框架，通过稳健的提示对齐增强零样本槽位推理。该框架采用分层LoRA架构实现逐层动态处理（融合低层启发式分组与高层全交互），集成谱联合领域-槽位聚类以识别可迁移关联（并输入自适应线性融合机制），同时引入语义增强奇异值分解初始化（SemSVD-Init）以保留预训练知识。在多领域数据集MultiWOZ和SGD上的实验表明，HiCoLoRA优于基线方法，在zs-DST中达到最优性能。代码已开源：https://github.com/carsonz/HiCoLoRA