VertMark embeds robust, training-free watermarks into vertical domain language models by creating hidden semantic equivalence between low-frequency triggers and high-frequency domain terms via parameter swaps, supporting reliable verification with negligible performance impact.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
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WiseOWL introduces a four-metric scoring system with a Streamlit app to evaluate and recommend ontologies for reuse based on descriptiveness and semantic correctness.
LLMs match or beat supervised BERT models on detecting whether a discharge note contains an actionable clinical task but trail on classifying the exact type of action, pointing to the need for datasets that explain why each span was labeled actionable.
FedSpy-LLM uses gradient decomposition and iterative alignment to reconstruct larger batches and longer sequences of training data from LLM gradients in federated settings, including with PEFT methods.
CLIN-LLM combines uncertainty-calibrated BioBERT classification with retrieval-augmented FLAN-T5 generation and safety post-processing to reach 98% accuracy on clinical cases while cutting unsafe antibiotic suggestions by 67%.
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CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation
CLIN-LLM combines uncertainty-calibrated BioBERT classification with retrieval-augmented FLAN-T5 generation and safety post-processing to reach 98% accuracy on clinical cases while cutting unsafe antibiotic suggestions by 67%.