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RADS uses reinforcement learning to pick the most informative samples for few-shot fine-tuning, boosting transfer-learning accuracy on low-resource, highly imbalanced clinical datasets.
This paper proposes ACSESS, a method for automatically combining multiple sample selection strategies to improve few-shot learning across both in-context learning and gradient-based approaches. The work demonstrates that combining strategies consistently outperforms individual selection methods across 14 datasets with both text and image modalities.