Recently, prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks. However, in practical dialogue scenarios, relying solely on simplistic templates and traditional corpora presents a challenge for these methods in generalizing to unknown input perturbations. To address this gap, we propose a multi-task demonstration based generative framework for noisy slot filling, named DemoNSF. Specifically, we introduce three noisy auxiliary tasks, namely noisy recovery (NR), random mask (RM), and hybrid discrimination (HD), to implicitly capture semantic structural information of input perturbations at different granularities. In the downstream main task, we design a noisy demonstration construction strategy for the generative framework, which explicitly incorporates task-specific information and perturbed distribution during training and inference. Experiments on two benchmarks demonstrate that DemoNSF outperforms all baseline methods and achieves strong generalization. Further analysis provides empirical guidance for the practical application of generative frameworks. Our code is released at https://github.com/dongguanting/Demo-NSF.

翻译：最近，基于提示的生成框架在序列标注任务中展现出令人瞩目的能力。然而，在实际对话场景中，仅依赖简单模板和传统语料库使得这些方法难以泛化到未知输入扰动。为解决这一局限，我们提出了一种面向嘈杂槽填充的多任务演示驱动生成框架DemoNSF。具体而言，我们引入了三个嘈杂辅助任务——噪声恢复（NR）、随机掩码（RM）和混合判别（HD）——以隐式捕获不同粒度下输入扰动的语义结构信息。在下游主任务中，我们设计了一种面向生成框架的噪声演示构建策略，该策略在训练和推理过程中显式融合任务特定信息与扰动分布。在两个基准数据集上的实验表明，DemoNSF优于所有基线方法，并实现了强大的泛化能力。进一步分析为生成框架的实际应用提供了经验性指导。我们的代码已发布在https://github.com/dongguanting/Demo-NSF。