This evaluation explores the In-context learning (ICL) capabilities of pre-trained language models on arithmetic tasks and sentiment analysis using synthetic datasets. The goal is to use different prompting strategies—zero-shot, few-shot, and chain-of-thought—to assess the performance of these models on the given tasks.