无需训练即可智能分配任务给专家，提升人机协作效率。
No Need for Learning to Defer? A Training Free Deferral Framework to Multiple Experts through Conformal Prediction
- 用置信区间量化不确定性，自动选最合适的专家
- 减少91.3%标注数据需求，低数据下仍保持准确率
- 免训练设计，节省99%训练时间，适合实际部署
AI系统在所有输入上难以提供可靠预测，促使人机协同决策的发展。现有学习型延迟（L2D）方法需大量专家标注数据并敏感于专家变动，导致重训成本高。本文提出一种无需训练、模型与专家无关的延迟框架，基于合取预测（conformal prediction）生成预测集，以标签特定不确定性为依据，通过区分度准则选择最优专家。在CIFAR10-H与HAM10000上的实验表明，该方法可将每专家所需训练标签减少高达91.3%，且在低数据场景下维持预测精度。因其免训练特性，训练时间降低两个数量级，为真实世界人机协作提供可扩展替代方案。
原文摘要 · Abstract (English)
AI systems often struggle to provide reliable predictions across all inputs, motivating hybrid human-AI decision-making. Existing Learning to Defer (L2D) approaches address this by training models to selectively defer to human experts. However, these approaches require extensive training data annotated by all experts and are sensitive to changes in expert composition, necessitating costly retraining. We propose a training-free, model- and expert-agnostic framework for expert deferral based on conformal prediction. Our method leverages prediction sets from a conformal predictor to quantify label-specific uncertainty and selects the most suitable expert using a segregativity criterion, which measures how well an expert discriminates among plausible labels. Experiments across three models on CIFAR10-H and HAM10000 demonstrate that our method can reduce the number of training labels per expert by up to 91.3% while maintaining predictive accuracy in low-data regimes. Being training-free, it also reduces training time by two orders of magnitude, offering a scalable, alternative to L2D for real-world human-AI collaboration.
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