Demonstration learning aims to guide the prompt prediction via providing answered demonstrations in the few shot settings. Despite achieving promising results, existing work only concatenates the answered examples as demonstrations to the prompt template (including the raw context) without any additional operation, neglecting the prompt-demonstration dependencies. Besides, prior research found that randomly replacing the labels of demonstrations marginally hurts performance, illustrating that the model could not properly learn the knowledge brought by the demonstrations. Inspired by the human learning process, in this paper, we introduce Imitation DEMOnstration Learning (Imitation-Demo) to strengthen demonstration learning via explicitly imitating human review behaviour, which includes: (1) contrastive learning mechanism to concentrate on the similar demonstrations. (2) demonstration-label re-prediction method to consolidate known knowledge. Experiment results show that our proposed method achieves state-of-the-art performance on 11 out of 14 classification corpora. Further studies also prove that Imitation-Demo strengthen the association between prompt and demonstrations, which could provide the basis for exploring how demonstration learning works.
翻译:示范学习的目的是通过在少数镜头环境中提供应答的演示来指导快速预测。尽管取得了可喜的成果,但现有工作仅将所回答的例子作为演示的快速模板(包括原始背景),而没有开展任何额外的行动,忽视了快速演示依赖性。此外,先前的研究发现,随机取代示威标签对表现有轻微伤害,表明模型无法正确了解示威带来的知识。在人类学习过程的启发下,我们在本文件中引入了 " 模拟脱钩学习 " (imitation-Demo),通过明确模仿人类审查行为来加强示范学习,其中包括:(1) 以对比学习机制集中关注类似的演示。(2) 示范标签重新定位方法,以巩固已知知识。实验结果显示,我们拟议的方法在14个分类公司中的11个实现了最新表现。进一步的研究还证明, " Imitation-Demo " 强化了快速与演示之间的联系,这可以为探索演示学习如何运作提供基础。