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University of California
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Entity embeddings outperform traditional encoding methods in high-cardinality fraud detection, achieving a record AUC-ROC score that could redefine best practices in the field.
LLM safety classifiers can be made dramatically more robust against jailbreaks by teaching them to "think twice" via lightweight, self-reflection fine-tuning.
