AINeutralarXiv – CS AI · 5h ago6/10
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ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
ReTabAD introduces a new benchmark dataset for tabular anomaly detection that incorporates semantic context through textual metadata, addressing a gap where existing datasets lack domain knowledge. The research provides 20 enriched datasets, implementations of classical and LLM-based detection algorithms, and demonstrates that semantic context improves both detection performance and interpretability.