Abstract: Information extraction (IE) aims to automatically identify and extract information about specific interests from raw texts. Despite the abundance of solutions based on fine-tuning pretrained language models, IE in the context of few-shot and zero-shot scenarios remains highly challenging due to the scarcity of training data. Large language models (LLMs), on the other hand, can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks. Nevertheless, it is unclear, whether such effectiveness can be replicated in the task of IE, where the target tasks involve specialized schema and quite abstractive entity or relation concepts. In this paper, we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods, including the structured fundamental prompt (SFP), the structured interactive reasoning prompt (SIRP), and the voting-enabled structured interactive reasoning prompt (VESIRP). The experimental results demonstrate that while directly promotes inferior performance, the proposed augmented prompt methods significantly improve the extraction accuracy, achieving comparable or even better performance (e.g., zero-shot FewNERD, FewNERD-INTRA) than state-of-the-art methods that require large-scale training samples. This study represents a systematic exploration of employing instruction-following LLM for the task of IE. It not only establishes a performance benchmark for this novel paradigm but, more importantly, validates a practical technical pathway through the proposed prompt enhancement method, offering a viable solution for efficient IE in low-resource settings.