查看文章

Few-shot learning is an area within the domain of machine learning that focuses on the challenge of training models capable of effectively performing new tasks using only a limited number of labeled instances. This contrasts standard machine learning approaches, wherein models are trained using large datasets of labeled instances. The problem of few-shot learning represents a significant challenge, yet its significance keeps rising due to the expanding volume of labeled data accessible for training machine learning models. This limitation arises from the fact that in numerous practical situations, it is often impossible to collect a large dataset comprising labeled instances for each specific task that requires resolution. The typical procedure involves two distinct stages: pre-training and fine-tuning. During the pre-training phase, a language model undergoes training using an extensive collection of textual data, which …