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Found 4 papers, 2 papers with code
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques.
Assertion Detection Large Language Model In-context Learning LoRA Fine-tuning
To further assess the generalizability of our approach, we extended our evaluation to a local dataset that focused on sleep concept extraction.
Improving Emotional Expression and Cohesion in Image-Based Playlist Description and Music Topics: A Continuous Parameterization Approach
Text generation in image-based platforms, particularly for music-related content, requires precise control over text styles and the incorporation of emotional expression.
Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning
Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge.
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