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Applications of Large Language Models in Pathology
BioengineeringLarge language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write ...
Jerome Cheng
doaj +3 more sources
Large Language Models in Gastroenterology: Systematic Review
Journal of Medical Internet ResearchBackgroundAs health care continues to evolve with technological advancements, the integration of artificial intelligence into clinical practices has shown promising potential to enhance patient care and operational efficiency ...
Eun Jeong Gong +7 more
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Large Language Models and Empathy: Systematic Review
Journal of Medical Internet ResearchBackgroundEmpathy, a fundamental aspect of human interaction, is characterized as the ability to experience another being’s emotions within oneself.
Vera Sorin +6 more
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BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models [PDF]
International Conference on Machine Learning, 2023 The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from
Junnan Li +3 more
semanticscholar +1 more source
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models [PDF]
International Conference on Learning Representations, 2023 The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images.
Deyao Zhu +4 more
semanticscholar +1 more source
Probabilistic medical predictions of large language models [PDF]
npj Digital MedicineLarge Language Models (LLMs) have shown promise in clinical applications through prompt engineering, allowing flexible clinical predictions. However, they struggle to produce reliable prediction probabilities, which are crucial for transparency and ...
Bowen Gu +3 more
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Tree of Thoughts: Deliberate Problem Solving with Large Language Models [PDF]
Neural Information Processing Systems, 2023 Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference.
Shunyu Yao +6 more
semanticscholar +1 more source
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling [PDF]
International Conference on Machine Learning, 2023 How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact ...
Stella Biderman +12 more
semanticscholar +1 more source
WizardLM: Empowering Large Language Models to Follow Complex Instructions [PDF]
arXiv.org, 2023 Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive.
Can Xu +7 more
semanticscholar +1 more source
A Survey on Evaluation of Large Language Models [PDF]
ACM Transactions on Intelligent Systems and Technology, 2023 Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes
Yu-Chu Chang +15 more
semanticscholar +1 more source