Large language models (LLMs) possess a fascinating ability called in-context learning (ICL), where they learn new tasks simply from examples within a prompt, without any formal training updates. But how do these models, trained on massive amounts of unstructured text like web pages, develop this skill? New research explores this puzzle, revealing surprising insights into what tasks LLMs can learn in context and the conditions that enable this learning. The study finds that for tasks like word analogies (e.g., "dog is to puppy as cat is to kitten"), the simple co-occurrence of words in the training data is enough for ICL to emerge. Even a basic model like continuous bag-of-words (CBOW), which doesn't consider word order, can perform these analogies if the word pairs appear together frequently enough. However, for more complex logic reasoning tasks, such as identifying the first letter of a word, the model needs to understand word order and positional information. Interestingly, the research also reveals scenarios where ICL fails, highlighting the importance of the training data's structure. For instance, if a model is trained on sentences where a pattern is always repeated (like "abacdcefe"), it struggles to generalize this "repetition" concept to new, unseen patterns. Similarly, if word pairs always appear in fixed positions in the training sentences, the model can't apply this knowledge when the pairs are presented in different arrangements during ICL. These findings suggest that while co-occurrence is sufficient for some tasks, more complex ICL relies on the model's ability to recognize and generalize patterns, which in turn depends heavily on how information is structured in the training data. This research opens up exciting new avenues for improving ICL by optimizing the structure and content of training data for LLMs.