Can few-shot learning and fine-tuning be combined?

Yes, few-shot learning and fine-tuning can indeed be effectively combined. This approach leverages the advantages of both techniques to enhance model adaptation, particularly when labeled data for a specific new task is extremely scarce.

Combining them typically involves fine-tuning a pretrained model on the new task first, utilizing whatever small labeled dataset is available. Subsequently, few-shot learning techniques (like prompt engineering or embedding-based methods) are applied on top of this fine-tuned model. The fine-tuning step tailors the model's parameters to the specific domain or task nuances, while the few-shot step allows for rapid adaptation to new examples or slight variations at inference time. Success depends on the base model's quality, the effectiveness of the initial fine-tuning data, and the design of the few-shot inference mechanism.

This combined strategy offers significant practical value. Implementation usually follows a sequence: 1) Base model pretraining, 2) Fine-tuning on the target task using available small data, 3) Prompt design/context setting for few-shot inference, 4) Deployment where the model processes new inputs presented alongside a few demonstrative examples for that specific query. This delivers both deep specialization from fine-tuning and the flexibility and data efficiency of few-shot learning, making it powerful for low-resource scenarios needing both accuracy and adaptability.