Learning with not Enough Data Part 3: Data Generation
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- Jul 21, 2026
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Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2 ). Let’s consider two approaches for generating synthetic data for training. Augmented data . Given a set of existing training samples, we can apply a variety of augmentation, distortion and transformation to derive new data points without losing the key attributes. We have covered a bunch of augmentation methods on text and images in a previous post on contrastive learning. For the sake of post completeness, I d
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If you're training or fine-tuning models with limited labeled data, this breaks down practical approaches to synthesizing training examples — augmentation techniques and few-shot LM-based generation — so you can bootstrap datasets without expensive labeling.
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