N"urnberg NLP at ChildSafeAds 2026: Structurally Dissimilar Voter Ensembles under Four Levels of Data Access

arXiv:2609.34986 · cs.CL, cs.LG · Submitted 2026-09-28 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Ablation — 1× in this paper · explained in 1 episode
• Abstention — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• Fourteenth International Conference on Learning Representations — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Phi-4 Technical Report
• ChildSafeAds Shared Task 2026: Commercial Content in Child-Facing YouTube Videos
• Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way
• AI Systems in Text-Based Online Counselling: Ethical Considerations Across Three Implementation Approaches

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