EchoDistill applies noisy-to-clean self-distillation with GRPO to boost Audio LLM robustness, reporting 4.18% average GSR gains under strong noise.
Robustness in large language models: A survey of mitigation strategies and evaluation metrics
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Proposes a multi-dimensional behavioral framework with six dimensions (Correctness, Consistency, Robustness, Local Logical Coherence, Efficiency, Stability) plus deployment-aware aggregation to diagnose LLM reasoning beyond accuracy-based benchmarks.
Introduces perturbation-based robustness evaluation and hybrid masking-adversarial training to reduce reliance on spurious topical cues while preserving methodological signals in biomedical publication type classification.
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.
Debiasing via fine-tuning can enhance LLM robustness to semantically neutral prompt perturbations by addressing perturbation-induced bias in neural network outputs.
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Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations
Introduces perturbation-based robustness evaluation and hybrid masking-adversarial training to reduce reliance on spurious topical cues while preserving methodological signals in biomedical publication type classification.