Does few-shot learning reduce training costs?

Few-shot learning can significantly reduce training costs in specific scenarios. This approach minimizes the need for large labeled datasets and extensive computation.

Key factors include the model's architecture, the relevance and quality of the few provided examples, and the similarity between the target task and the model's pre-training data. It excels when examples are highly representative and tasks are well-defined. However, effectiveness isn't guaranteed; substantial engineering effort for prompt/example design is often required, and performance may not match data-intensive methods.

Its primary value lies in adapting models quickly to niche domains, rare classes, or rapidly changing requirements with minimal labeling expense. This enables faster prototyping and deployment where collecting large datasets is impractical or costly, reducing both data acquisition and compute resources. It serves best as an efficient fine-tuning strategy rather than eliminating training costs entirely.