What are the limitations of few-shot learning?

Few-shot learning allows models to adapt to new tasks with minimal examples, but it has significant limitations. The primary challenges stem from reliance on pre-training data, difficulty handling domain shifts, and high computational demands. These constraints affect real-world usability, especially in scenarios requiring robustness or efficiency.

First, few-shot learning depends heavily on the quality and diversity of pre-training data. Models like GPT-3 or CLIP perform well because they’re trained on vast, varied datasets. However, if the target task is outside the pre-training domain, performance drops sharply. For example, a model trained on general text might struggle with medical terminology even if given a few examples. This reliance means developers must either invest in expansive datasets or accept limited applicability. Additionally, biases in pre-training data—such as underrepresentation of certain languages or cultural contexts—can propagate into few-shot tasks, leading to unreliable outputs.

Second, few-shot methods struggle with domain adaptation. If the new task differs significantly from the pre-training domain, the model may fail to generalize. For instance, a vision model trained on natural images might not recognize specialized industrial defects in machinery, even with a handful of examples. This limitation forces developers to either collect more data—defeating the purpose of few-shot learning—or redesign the model. Tasks requiring fine-grained distinctions (e.g., differentiating bird species) are particularly vulnerable, as subtle features may not be captured without extensive training.

Finally, few-shot models often require massive computational resources. Architectures like transformers demand significant memory and processing power, making them impractical for edge devices or low-budget projects. Training or fine-tuning these models can be prohibitively expensive. For example, running a large language model like GPT-3 for few-shot inference costs thousands of dollars monthly at scale. Smaller teams may lack the infrastructure to deploy such systems, limiting accessibility. While techniques like model distillation can reduce size, they often sacrifice performance, undermining the benefits of few-shot learning. These resource constraints highlight a trade-off between capability and practicality.