  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.
