Map GPT Playground: Smart Locations and Routes with GPT

Chiqun Zhang, Antonios Karatzoglou, Helen Craig, Dragomir Yankov

Abstract: People often ask questions for which the answer contains multiple locations or locations with additional context. The questions can be very natural and easy to understand by other people, yet if formulated as map queries, today's map search engines struggle to understand them. Here we look into three categories of such map queries: 1) queries with multiple explicitly stated locations, e.g. 'Show me directions from A to B through C, D, and E'; 2) queries where the locations need to be inferred, e.g. 'Show me on the map all locations which James Bond visited in Casino Royale'; 3) queries of locations where we request additional geographical, historical or other context, e.g. 'Show me a map of wildlife in Australia'. We build a prototype system, called Map GPT Playground, and demonstrate with it how such queries can be seamlessly answered by combining the power of Large Language Models (LLM) with foundational maps services, such as geocoding, routing, etc. We describe the architecture of the system and reason over the abstractions needed for the system to be able to generalize across complex query intents and invoke suitable chains of services to fulfil them. Lastly, we demonstrate that in resolving complex location search queries, novel considerations emerge without prior analog, namely the set of returned locations needs to be spatially consistent and often to satisfy some inferred from the query temporal order.

External IDs:dblp:conf/gis/ZhangKCY23