Abstract
Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning or few-shot learning, aims to effectively train a model using only a small amount of labelled samples. However, these approaches have been observed to be excessively sensitive to the effects of uncontrolled randomness caused by non-determinism in the training process. The randomness negatively affects the stability of the models, leading to large variances in results across training runs. When such sensitivity is disregarded, it can unintentionally, but unfortunately also intentionally, create an imaginary perception of research progress.