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Found 5 papers, 2 papers with code
LLMs in Biomedicine: A study on clinical Named Entity Recognition
Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedicine due to medical language complexities and data scarcity.
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering
Our method uses the idea of a classification model explainer to generate questions and answers about medical concepts corresponding to medical codes.
Surpassing GPT-4 Medical Coding with a Two-Stage Approach
Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations.
Extend and Explain: Interpreting Very Long Language Models
While Transformer language models (LMs) are state-of-the-art for information extraction, long text introduces computational challenges requiring suboptimal preprocessing steps or alternative model architectures.
Pretraining Federated Text Models for Next Word Prediction
Federated learning is a decentralized approach for training models on distributed devices, by summarizing local changes and sending aggregate parameters from local models to the cloud rather than the data itself.
