Requirements elicitation and modeling is a critical step in requirements engineering that affects subsequent system design and implementation. Traditional requirements elicitation and modeling methods usually involve multiple types of stakeholders, such as requirement providers and requirements analysts, working together in an iterative manner, which requires a lot of manpower. It is important to reduce the burden of requirements providers and requirements analysts and improve the efficiency of elicitation and modeling. Some of the existing efforts use knowledge bases to provide more knowledge to assist modeling or elicitation, and some use natural language processing techniques to automate the elicitation or modeling process. But they do not reduce the burden of requirements providers. Leveraging the generative capability of LLMs (large language models), we provide ChatModeler, a requirement elicitation and modeling framework for iterative human-machine collaboration. Specifically, based on the division of labor and collaboration relationship of real-world requirements teams, some work of requirements providers and requirements analysts is taken up by LLMs, while requirements providers only need to perform confirmation. In this paper, prompts are designed for the various roles played by the various LLMs, which varies with the meta-model of the requirements. ChatModeler is used in 14 sets of comparative experiments with state-of-the-art automated modeling approaches for three types of requirement models on seven requirement cases, demonstrating the superiority of ChatModeler in both reducing the number of interactions of the requirements provider and generating higher quality requirement models.