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However, these models can be noisy, particularly in the medical domain, so we need approaches that mitigate this noise but are still able to leverage these models’ strengths. In this context, our approach of infusing medical knowledge in pretrained models such as GPT-3 to generate high-quality synthetic labels is an idea with wide applicability in low-resource settings like healthcare. As deep models usually require a large amount of data to perform accurately and robustly, this deters their widespread application in healthcare. In parallel, there has been a lot of progress in development of large scale models leveraging web-scale data, such as GPT-3, that show good low-shot performance.
