Fine-tuning Data Efficiency › Summary

This chapter explored techniques for maximizing the value of limited labeled data in fine-tuning.

The sample efficiency spectrum ranges from zero-shot inference through few-shot fine-tuning to large-scale adaptation. Pre-trained models fundamentally change the relationship between data quantity and model performance, letting strong results with far fewer examples than traditional approaches require.

Few-shot fine-tuning methods like Pattern-Exploiting Training (PET) reformulate tasks to match pre-training objectives, while SetFit uses contrastive learning to amplify few-shot data through pair generation. These techniques can reach strong performance with just 8-32 labeled examples per class.

Data augmentation techniques for text include synonym replacement, back-translation, random operations (deletion, swap, insertion), embedding-space mixup, and contextual augmentation using language models. While less straightforward than image augmentation, these methods can substantially expand small datasets.

Sample efficiency patterns emerge from pre-training, which gives strong linguistic priors that reduce the need for task-specific data. Features learned from large unlabeled corpora transfer effectively to downstream tasks with minimal adaptation.

Small data strategies emphasize domain-aligned model selection, task reformulation, aggressive regularization, ensembling across random seeds, and considering the trade-off between label efficiency and model size.

The next part explores parameter-efficient fine-tuning methods like LoRA, which extend these efficiency principles to model adaptation itself, letting large models to be customized with minimal computational cost and memory overhead.