Is the few-shot learning effect stable?

The stability of few-shot learning results is generally not guaranteed and can vary significantly. While feasible, achieving consistent outcomes is challenging compared to methods using large labeled datasets.

Stability hinges on several critical factors: the foundational model architecture and its pre-training quality, the representativeness and compatibility of the provided limited examples with the target task, the algorithm's inherent robustness, and the skill in prompt or demonstration design. Techniques like meta-learning or transfer learning can enhance stability. Performance often degrades as task complexity increases or when the target domain diverges substantially from the model's pre-training data. Domain-specific foundation models tend to offer more stable few-shot performance within their domain.

Despite instability concerns, few-shot learning offers significant value in low-resource scenarios like niche NLP tasks (classification, generation) and specialized computer vision applications. Its core application lies in rapidly adapting models to new tasks with minimal labeled data, enabling quicker deployment and reduced annotation costs. Careful experiment design and technique selection are crucial for reliable use.