cross-domain-generalization

This paper investigates instruction-tuning general-purpose LLMs for robust harmful content mitigation, specifically hate speech detection, using a unified corpus of 36 datasets, achieving state-of-the-art performance and enhanced cross-domain and cross-lingual generalization.

This paper proposes a diagnosis and optimization framework for cross-domain and cross-lingual generalization in offensive language detection, decomposing performance degradation into dataset and language effects, and quantifying the trade-off between multilingual capability and source-task performance.

This paper investigates the cross-domain generalization failure of lightweight ML models for IIoT intrusion detection, finding they rely on coarse port features and that adversarial robustness does not correlate with cross-network performance.