The research on large-scale multi- and many-objective optimization has received increasing attention in the evolutionary multi-objective optimization (EMO) community. A number of large-scale EMO algorithms based on different strategies (e.g., divide-and-conquer, coevolution, and dimensionality reduction) have been proposed over the last decade. The performance of the large-scale EMO algorithms was empirically evaluated using several benchmark test suites, including the ZDT, DTLZ, WFG, MaF, UF and LSMOP test suites. Even though these test suites are theoretically scalable to any number of decision variables, they are not necessarily appropriate for examining the performance of large-scale EMO algorithms. In fact, among these benchmark test suites, only the LSMOP test suite is specifically designed to test the performance of large-scale EMO algorithms. In this paper, we propose a new scalable multi- and many-objective test problem for examining large-scale EMO algorithms. The proposed test problem has the following features: 1) the number of objectives and decision variables can be arbitrarily specified; 2) the interaction strength among the objectives can be adjusted by a correlation parameter. The performance of six EMO algorithms is examined on the new test problem. Our experimental results show that the proposed new test problem poses difficulties to some state-of-the-art large-scale EMO algorithms.