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Found 9 papers, 4 papers with code
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks
Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details.
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Instruction-finetuning (IFT) has become crucial in aligning Large Language Models (LLMs) with diverse human needs and has shown great potential in medical applications.
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments.
Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization
Text summarization has been a crucial problem in natural language processing (NLP) for several decades.
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text
Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction.
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Our method first addresses irregularity in each single modality by (1) modeling irregular time series by dynamically incorporating hand-crafted imputation embeddings into learned interpolation embeddings via a gating mechanism, and (2) casting a series of clinical note representations as multivariate irregular time series and tackling irregularity via a time attention mechanism.
Explanations from Large Language Models Make Small Reasoners Better
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.
Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks
Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients.
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients
However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes.
