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Evaluating the ability of AI models to generate level-specific medical MCQs with variable difficulty. [PDF]
Al-Lawama M, Altamimi O, Altamimi E.
europepmc +1 more source
DescriptorMedSAM: language-image fusion with multi-aspect text guidance for medical image segmentation. [PDF]
Zhang W, Luo L, He M, Hai J, Ye J.
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Retrieval-Augmented Language Models Enable Scalable Chemical Source Classification in Metabolomics Workflows. [PDF]
Rajkumar P +12 more
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Learning to Prompt for Vision-Language Models
International Journal of Computer Vision, 2021Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks.
Kaiyang Zhou +3 more
semanticscholar +1 more source
Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs
The use of large language models (LLMs) in clinical medicine is currently thriving. Effectively transferring LLMs’ pertinent theoretical knowledge from computer science to their application in clinical medicine is crucial.
exaly +2 more sources
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
International Conference on Learning Representations, 2023Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset.
Qihang Zhou +4 more
semanticscholar +1 more source
AI literacy and its implications for prompt engineering strategies
Artificial intelligence technologies are rapidly advancing. As part of this development, large language models (LLMs) are increasingly being used when humans interact with systems based on artificial intelligence (AI), posing both new opportunities and ...
Andreas Janson
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