Data Augmentation

Full definition

Image: random crop, flip, rotation, color jitter, RandAugment, Mixup, CutMix. Text: synonym replacement, back-translation, EDA, LLM-paraphrase. Audio: SpecAugment, time-stretch, pitch-shift. Augmentation is one of the cheapest accuracy lifts available — typical gains are 1-5% on top of a baseline. For LLMs, augmentation increasingly means LLM-generated paraphrases or counterfactual edits.

Why it matters

Data augmentation is the standard answer to "we don't have enough labels and can't afford more." It also reduces over-fitting and improves robustness — both of which matter for production reliability and for passing red-team evaluations.

Example

A document-classification model with 3,000 labeled emails uses LLM-paraphrasing to generate 4 variants per email, raising effective dataset size to 15,000 and lifting test F1 by 4 points.

Related terms

• Synthetic Data
  Synthetic data is artificially generated training data — produced by AI models or simulators rather than collected from the real world — used to augment, replace, or supplement real datasets while preserving privacy or covering rare cases.
• Data Labeling
  Data labeling is the process of attaching ground-truth annotations to raw data — text, images, audio — so a supervised model can learn from it, ranging from yes/no classification to structured extraction to multi-turn preference comparisons.
• Training Data
  Training data is the curated dataset used to teach a machine-learning model — every pattern the model can recognize, every bias it inherits, and every limit to its accuracy ultimately traces back to this data.
• Fine-tuning
  Fine-tuning is the process of further training a pretrained model on a smaller, task-specific dataset so it specializes in a particular style, domain, or behavior — without retraining from scratch.