用少量示范生成专家嵌入，实现高效自适应拒答系统
Learning To Defer To A Population With Limited Demonstrations
- 基于元学习从少量示范中生成专家特定嵌入
- 合成伪标签训练模型，逼近理想性能
- 适合需要快速适配新专家的实时人机协作场景
本文针对学习拒答（L2D）系统在实际部署中面临的数据稀缺问题，提出一种上下文感知的半监督框架。该框架利用元学习仅从少量示范中生成专家特定嵌入，并设计双重机制：先用这些嵌入生成大规模伪标签用于训练，再在测试时实现对新专家的即时适配。在三个不同数据集上的实验表明，基于合成标签训练的模型能迅速接近理想性能，验证了方法的数据高效性。本工作解决了关键的训练瓶颈，使自适应L2D系统更实用、可扩展，为真实环境中的人机协作铺平道路。代码与训练配置已开源。
原文摘要 · Abstract (English)
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.
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