Interpreting and resolving map-related queries using a language model

A computing system for processing a query that relates to a map, comprising:

• an instruction data store for storing computer-readable instructions, the computer-readable instructions providing a control computer program that orchestrates functions performed by the computing system;
• and
• a processing system for executing the computer-readable instructions in the data store, to perform operations including:
• receiving the query;
• generating a prompt to submit to a machine-trained language model, the prompt including a series of tokens, the prompt expressing the query and describing a task that the machine-trained language model is being asked to perform;
• sending the prompt to the machine-trained language model;
• receiving a response generated by the machine-trained language model based on the prompt, the response identifying one or more intents of a predetermined set of intents that are relevant to the query;
• and
• in a multiplexing operation, invoking a particular processing pipeline based on the one or more intents identified by the machine-trained model, wherein the particular processing pipeline is one of plural context-specific processing pipelines executed by the control computer program,
• wherein at least one context-specific processing pipeline of the plural context-specific pipelines calls the machine-trained language model to classify each of two or more entities specified by the query as a primary entity or a secondary entity, wherein the primary entity has a higher priority than the secondary entity, indicating that the machine-trained language model is to treat the primary entity as an anchor, and then select the secondary entity from among entities that are located near the primary entity,
• wherein at least one context-specific processing pipeline of the plural context-specific pipelines includes:
• identifying, using a routing engine of a multi-engine system, one or more proposed routes based on the query;
• and validating each proposed route by determining whether the proposed route has a spatial distribution of locations that satisfies a prescribed test, and/or determining whether the proposed route has a travel time that is within a prescribed temporal deviation from a reference travel time, and
• wherein at least one context-specific processing pipeline of the plural context-specific pipelines generates an interactive map pertaining to the query, and presents an image that has been retrieved in prescribed proximity to the interactive map.

A computer-readable storage medium for storing computer-readable instructions, a processing system executing the computer-readable instructions to perform operations, the operations comprising:

• receiving the query;
• generating a prompt to submit to a machine-trained language model, the prompt including a series of tokens, the prompt expressing the query and describing a task that the machine-trained language model is being asked to perform;
• sending the prompt to the machine-trained language model;
• receiving a response generated by the machine-trained language model based on the prompt, the response identifying one or more intents of a predetermined set of intents that are relevant to the query;
• and
• in a multiplexing operation, invoking a particular processing pipeline based on the one or more intents identified by the machine-trained model, wherein the particular processing pipeline is one of plural context-specific processing pipelines executed by the control computer program,
• wherein at least one context-specific processing pipeline of the plural context-specific pipelines calls the machine-trained language model to classify each of two or more entities specified by the query as a primary entity or a secondary entity, wherein the primary entity has a higher priority than the secondary entity, indicating that the machine-trained language model is to treat the primary entity as an anchor, and then select the secondary entity from among entities that are located near the primary entity,
• wherein at least one context-specific processing pipeline of the plural context-specific pipelines includes:
• identifying, using a routing engine of a multi-engine system, one or more proposed routes based on the query;
• and validating each proposed route by determining whether the proposed route has a spatial distribution of locations that satisfies a prescribed test, and/or determining whether the proposed route has a travel time that is within a prescribed temporal deviation from a reference travel time, and
• wherein at least one context-specific processing pipeline of the plural context-specific pipelines generates an interactive map pertaining to the query, and presents an image that has been retrieved in prescribed proximity to the interactive map.