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This paper investigates how different types of training data, specifically didactic knowledge and clinical cases, affect the capabilities of medical large language models, revealing asymmetric improvements and advocating for application-driven data curation.
Percy Liang shares an overview of the data composition for the Harrier K40 training run, detailing domain clusters and quality buckets as part of the Marin team's joint effort.