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Found 7 papers, 0 papers with code
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Large language models (LLMs) hold immense promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities.
Augmentations vs Algorithms: What Works in Self-Supervised Learning
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL).
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations
We define augmentations in frequency space called Fourier Domain Augmentations (FDA) and show that training SSL models on a combination of these and image augmentations can improve the downstream classification accuracy by up to 1. 3% on ImageNet-1K.
Towards Generalist Biomedical AI
While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.
Towards Expert-Level Medical Question Answering with Large Language Models
Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67. 2% on the MedQA dataset.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #4 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Contrastive Learning for Label-Efficient Semantic Segmentation
While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases.
