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.

本文提出了模仿式演示学习（Imitation-Demo）方法，通过对人类审查行为的明确模仿来加强演示学习，进而增强提示和演示之间的关联性，从而在 14 个分类语料库中取得了显著的性能提升。

让我来检查这些示例：通过显式模仿增强示范学习

Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation

Humans have the capability, aided by the expressive compositionality of their
language, to learn quickly by demonstration. They are able to describe unseen
task-performing procedures and generalize their execution to other contexts. In
this work, we introduce DescribeWorld, an environment designed to test this
sort of generalization skill in grounded agents, where tasks are linguistically
and procedurally composed of elementary concepts. The agent observes a single
task demonstration in a Minecraft-like grid world, and is then asked to carry
out the same task in a new map. To enable such a level of generalization, we
propose a neural agent infused with hierarchical latent language--both at the
level of task inference and subtask planning. Our agent first generates a
textual description of the demonstrated unseen task, then leverages this
description to replicate it. Through multiple evaluation scenarios and a suite
of generalization tests, we find that agents that perform text-based inference
are better equipped for the challenge under a random split of tasks.

使用基于描述性语言的学习方法，研究设计了一个环境来测试智能体的泛化技能，并通过多个测试评估方案和测试集发现，在随机任务拆分的情况下，文本推理的智能体更适合应对这一挑战。