Searching for directions is one of the most used features of map applications. This paper shares our vision on how Direction Services will change, with LLM-based chat assistants rapidly becoming an integral part of the underlying path search mechanism. We anticipate an influx of more complex, conversational route planning sessions, where users colloquially describe route-related preferences as if they were talking to their personal chauffeur. We envision future systems able to support asks like "avoid the East River tunnel", "take the bridge", or "find me a scenic route around the lake, oh and by the way, I'm driving the EV today". At present, popular map search engines fail in even simple, yet very natural preferences, such as 'take me from A to B via road C'. The reason is mainly twofold, inadequate query understanding and lack of mechanisms in routing to satisfy this type of preferences. The here proposed solution is a novel treatment of routing, one which casts it into an end-to-end 2-layer relevance framework. The framework is capable of performing query understanding for route queries with complex preferences and intents. It treats routes as richly annotated documents and the routing engine, in addition to performing optimization, acts (1) as a retriever of route documents that match the user intent and (2) as a ranker that ranks route candidates not just by a simple time-distance cost model, but by inferring the importance of many variables, some derived from explicitly stated preferences and others identified as relevant through data-driven methodology.

Understanding map queries and retrieving correct entity results are the two main relevance tasks in Map search. They are usually performed by a set of task specific machine learning models. Collecting large amount of high quality labelled data for training such models is a time-consuming and labor-intensive process. Although various methods have been studied for producing pseudo data labels, they are limited in their effectiveness when applied across different languages or tasks. The recently released Large Language models (LLMs), including ChatGPT and GPT-4 (GPT for short), have demonstrated state-of-the-art performance in text understanding by using simple prompt instructions with only a handful of examples for in-context learning. In this paper, we explore GPT as a cost-effective alternative for both data labeling and synthetic data generation, where we subsequently use data obtained from this approach to train various task specific models such as maps intent detection, address detection, address parsing, geo-entity ranking, and rank scores calibration. GPT demonstrates strong potential in generating otherwise hard-to-synthesize data. We observe significant accuracy and relevance improvement across all task specific models when trained or fine-tuned on data generated by GPT. Lastly, we propose a general framework combining labeled data from GPT with other sources and a prompt fine-tune structure to guide GPT model in completing a given task.

Correct understanding of direction queries is essential in map search for providing accurate direction related results, including routing, travel distance, travel time estimation, etc. Slot tagging is the process of recognizing and annotating query terms as entities such as source, destination, travel mode, travel distance, or travel time, so that downstream map search components can surface the expected result. Transformer-based models have achieved state-of-the-art performance on various language understanding tasks, including slot tagging. However, such models require either good quality labeled data for fine-tuning or large amount of labeled data for full training. Active learning provides a solution for improving training efficiency by selecting only a small amount of very informative queries for labelling. It is not yet clear, though, how to properly apply active learning for transformer-based language models. In this paper, we propose a novel active learning method designed specifically for transformer models and demonstrate its effectiveness for slot tagging of direction queries.