Controlled semantic perturbations and selective robustness training with entity masking and adversarial objectives mitigate the typical robustness-accuracy trade-off in publication type and study design classification.
Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics
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Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.
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Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations
Controlled semantic perturbations and selective robustness training with entity masking and adversarial objectives mitigate the typical robustness-accuracy trade-off in publication type and study design classification.
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Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts
Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.