Language models exhibit an emergent ability to learn a new task from a small number of input-output demonstrations. However, recent work shows that in-context learners largely rely on their pre-trained knowledge, such as the sentiment of the labels, instead of learning new associations from the input. We argue that the commonly-used few-shot evaluation using a random selection of in-context demonstrations can not disentangle models' reliance on such biases, as most of the randomly-selected demonstrations do not present relations informative for prediction beyond exposing the task's input-output distribution. Therefore, to evaluate models' in-context learning ability independent of models' memory, we introduce a Concept-sharing few-shot learning method choosing the demonstrations that share an underlying concept with the predicted sample. We extract a set of such concepts from available human explanations and measure how much models can benefit from presenting these concepts in few-shot demonstrations. We find that most of the recent in-context learners can not consistently benefit from the demonstrated concepts, irrespective of the model size. However, we note that T0 models are more sensitive to exhibited concepts, benefiting from concept-sharing demonstrations in 7 out of 8 evaluation scenarios.

翻译：语言模型展现出从少量输入-输出样本中学习新任务的涌现能力。然而，近期研究表明，上下文学习者在很大程度上依赖其预训练知识（如标签的情感倾向），而非从输入中学习新的关联。我们认为，目前广泛采用的随机选取演示样本的少样本评估方法，无法分离模型对此类偏差的依赖——因为大多数随机选取的演示样本除了暴露任务的输入-输出分布外，并未呈现对预测有用的关联信息。为此，为评估模型独立于记忆的上下文学习能力，我们提出一种概念共享的少样本学习方法：选择与预测样本共享潜在概念的演示样本。我们从现有的人工解释中提取此类概念集合，并衡量在少样本演示中呈现这些概念时，模型能从中获益的程度。研究发现，当前大部分上下文学习器无法持续受益于所展示的概念，且该现象与模型规模无关。值得注意的是，T0系列模型对呈现的概念更为敏感，在8个评估场景中的7个中能从概念共享演示中获益。