L2CU: Learning to Complement Unseen Users

• URL: http://arxiv.org/abs/2601.06119v1
• Date: Sat, 03 Jan 2026 20:30:48 GMT
• Title: L2CU: Learning to Complement Unseen Users
• Abstract summary: We introduce L2CU, a novel framework for human-AI cooperative classification with unseen users.<n>Given sparse and noisy user annotations, L2CU identifies representative annotator profiles capturing distinct labeling patterns.<n>By matching unseen users to these profiles, L2CU leverages profile-specific models to complement the user and achieve superior joint accuracy.
• Score: 15.25018543473196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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