Engineering Prompts for Spatial Questions

Large Language Models (LLMs) are often used for tasks that involve reasoning about the physical world, like recommending travel itineraries. However, success at these tasks requires the LLM to have been exposed to the relevant places, which is not true for lesser-known or alternatively named places, like Indigenous place names. Our prompting technique handles this issue using Retrieval Augmented Generation, encoding a spatial graph of common places and a mapping to their Indigenous alternatives. Our method improves LLM performance on spatial tasks involving lesser-known place names, thus advancing AI fairness.