This paper studies an accessible method for robot task assignment. We investigate how to combine two common end-user programming paradigms: drag-and-drop interfaces and natural language programming. Specifically, we construct a large-scale language model (LLM)-based pipeline that takes natural language as input and outputs human-like task sequences at a human-like granularity, and compare the generated task sequences to a dataset of manually assigned task sequences. Experimental results show that larger models perform better in generating human-like task sequences, but smaller models also achieve satisfactory performance.