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This paper presents an end-to-end hybrid framework for rumour detection in low-resource Algerian dialect social media content, achieving an F1-score of 0.84 by combining transformer embeddings with a classical classifier.
Researchers from Southampton and Manchester propose a hybrid adversarial defence framework for LLMs that combines entropy-based, uncertainty-based, and geometric-based models to simultaneously address hallucination and adversarial vulnerability in NLU tasks, achieving up to 64.92% improvement in adversarial robustness and 62.27% reduction in attack success rate.
This paper presents a hybrid framework that combines structured clinical data with LLM-generated narratives for coronary artery disease prediction, achieving high fidelity in variable extraction and comparing ML models with LLM-based zero-shot and few-shot classification.