As the scale of projects expands, the concurrent development model adopted by the open source community leads to an increasingly prominent problem of repetitive pull requests (PRs). The large number of rejections caused by duplicate pull requests increases the review workload of project maintainers and reduces the efficiency of pull request review. Therefore, it is very necessary to conduct automated duplicate PR detection. In this study, we propose DupLLM, a framework designed to detect duplicate PRs. The framework generates refined summaries by feeding the content of individual PRs into a large language model (LLM). Subsequently, the resulting summary is vectorized, converting the textual content into a numerical representation. The similarity between PRs is evaluated by calculating the similarity score between PR summary vectors. Ultimately, the model showed better performance than the best existing model, achieving an effect of 0.929 on P@1. This confirms that LLM can also achieve equivalent results in the field of duplicate PR detection as deep learning is used to train on this task, providing a new direction for the application of LLM in the field of software engineering.