Context-aware Conversational Map Search with LLM

Chiqun Zhang, Anirudh Sriram, Kuo-Han Hung, Renzhong Wang, Dragomir Yankov

Abstract: In the realm of map search engines, most are designed as semi-structured information retrieval systems, processing input queries that include text, user location, and viewport. These engines are adept at handling standalone queries, but they struggle with contextual queries, which are increasingly important in the era of conversational searches and require the model to utilize context beyond a single query. This study introduces a novel context-aware map search system, designed to extract, understand, and leverage context in map searches. We also propose an automatic evaluation system, underpinned by multiple Large Language Model (LLM) agents for investigating the trade-off of asking clarifying questions in conversational map search. Our demonstrations and experiments show that the proposed context-aware map search system can support a wide range of conversational searches. Additionally, our automatic evaluation system delivers quality judgments comparable to human evaluations, but at a significantly reduced cost.

External IDs:dblp:conf/gis/ZhangSHWY24