  Instruction generation is a vital and multidisciplinary research area with
broad applications. Existing instruction generation models are limited to
generating instructions in a single style from a particular dataset, and the
style and content of generated instructions cannot be controlled. Moreover,
most existing instruction generation methods also disregard the spatial
modeling of the navigation environment. Leveraging the capabilities of Large
Language Models (LLMs), we propose C-Instructor, which utilizes the
chain-of-thought-style prompt for style-controllable and content-controllable
instruction generation. Firstly, we propose a Chain of Thought with Landmarks
(CoTL) mechanism, which guides the LLM to identify key landmarks and then
generate complete instructions. CoTL renders generated instructions more
accessible to follow and offers greater controllability over the manipulation
of landmark objects. Furthermore, we present a Spatial Topology Modeling Task
to facilitate the understanding of the spatial structure of the environment.
Finally, we introduce a Style-Mixed Training policy, harnessing the prior
knowledge of LLMs to enable style control for instruction generation based on
different prompts within a single model instance. Extensive experiments
demonstrate that instructions generated by C-Instructor outperform those
generated by previous methods in text metrics, navigation guidance evaluation,
and user studies.
