While Large Language Models (LLMs) are known for their In-Context Learning (ICL) capabilities, there is no consensus on the underlying mechanisms. A key point of debate is whether ICL allows models to adapt to unseen tasks without parameter updates—that is, whether they can extrapolate. In this study, we address this question by constructing an arithmetic dataset based on the bivariate linear function z=ax+by to train a model and quantitatively evaluate its interpolation and extrapolation abilities through ICL. Our results show that while extrapolation was not achieved within our experimental design, tasks that were partially learned could be solved. We also found that the model acquires internal representations that can distinguish unseen tasks, and that greater task diversity in the training dataset improves ICL capabilities.