This paper addresses the critical data scarcity that hinders the practical deployment of learning to defer (L2D) systems to the population. We introduce a context-aware, semi-supervised framework that uses meta-learning to generate expert-specific embeddings from only a few demonstrations. We demonstrate the efficacy of a dual-purpose mechanism, where these embeddings are used first to generate a large corpus of pseudo-labels for training, and subsequently to enable on-the-fly adaptation to new experts at test-time. The experiment results on three different datasets confirm that a model trained on these synthetic labels rapidly approaches oracle-level performance, validating the data efficiency of our approach. By resolving a key training bottleneck, this work makes adaptive L2D systems more practical and scalable, paving the way for human-AI collaboration in real-world environments. To facilitate reproducibility and address implementation details not covered in the main text, we provide our source code and training configurations at https://github.com/nil123532/learning-to-defer-to-a-population-with-limited-demonstrations.

翻译：本文针对学习延迟决策系统在实际部署到群体时面临的关键数据稀缺问题展开研究。我们提出了一种上下文感知的半监督框架，该框架利用元学习技术，仅需少量演示样本即可生成专家特定嵌入向量。我们论证了一种双重作用机制的有效性：这些嵌入向量首先用于生成大量伪标签以进行模型训练，随后在测试阶段实现对新专家的实时适应。在三个不同数据集上的实验结果表明，基于这些合成标签训练的模型能够快速逼近理论最优性能，验证了本方法的数据高效性。通过解决关键训练瓶颈，本工作使自适应学习延迟决策系统更具实用性与可扩展性，为现实环境中的人机协作铺平道路。为促进可复现性并补充正文未涵盖的实现细节，我们在 https://github.com/nil123532/learning-to-defer-to-a-population-with-limited-demonstrations 提供了源代码与训练配置。