让AI识别陌生用户标签习惯，针对性补位提升人机协作准确率
L2CU: Learning to Complement Unseen Users
- 基于用户标注模式聚类生成代表性标签画像
- 在五个数据集上实现比基线更高的联合分类准确率
- 适合需要适配新用户的交互式人机协作场景
近期研究揭示了机器学习模型可学习弥补（L2C）人类优势的潜力，但将此能力推广至未见过的用户仍具挑战。现有L2C方法通过单一全局用户模型简化人机交互，忽略了个体差异，导致协作性能不佳。为此，我们提出L2CU框架，用于人机协同分类任务中的未见用户场景。面对稀疏且噪声较多的用户标注，L2CU识别出捕捉不同标注模式的代表性标注者画像。通过将未见用户匹配至这些画像，利用画像特异性模型实现对用户的互补，从而获得更优的联合准确率。我们在多个数据集（CIFAR-10N、CIFAR-10H、Fashion-MNIST-H、Chaoyang 和 AgNews）上评估了L2CU，验证了其作为模型无关方案在提升人机协同分类效果上的有效性。
原文摘要 · Abstract (English)
Recent research highlights the potential of machine learning models to learn to complement (L2C) human strengths; however, generalizing this capability to unseen users remains a significant challenge. Existing L2C methods oversimplify interaction between human and AI by relying on a single, global user model that neglects individual user variability, leading to suboptimal cooperative performance. Addressing this, we introduce L2CU, a novel L2C framework for human-AI cooperative classification with unseen users. Given sparse and noisy user annotations, L2CU identifies representative annotator profiles capturing distinct labeling patterns. By matching unseen users to these profiles, L2CU leverages profile-specific models to complement the user and achieve superior joint accuracy. We evaluate L2CU on datasets (CIFAR-10N, CIFAR-10H, Fashion-MNIST-H, Chaoyang and AgNews), demonstrating its effectiveness as a model-agnostic solution for improving human-AI cooperative classification.
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