Language Model Map Query Routing for Flexible Intent Handling
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Solution Overview
Problem
Existing map-related inquiry engines require specific input formats and are not adaptable to diverse user queries, leading to increased resource consumption, latency, and high development and maintenance costs.
Innovation Solution
A machine-trained language model is used to interpret complex map-related queries, integrating with geocoding, route finding, and image retrieval engines, allowing for flexible and extensible processing of queries in various formats without requiring engine-specific input formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the complexity of the program is increased to accommodate diverse inquiry scenarios, then the adaptability of the engine improves, but the resource consumption increases and latency performance worsens
Solution Approach 1:
The system segments the processing architecture into a language model component and multiple specialized processing engines (routing engine, geocoding engine, search engine, image retrieval engine). The language model handles diverse query interpretation while specialized engines handle specific tasks efficiently, avoiding the need to increase complexity within a single monolithic program.
Solution Approach 2:
The language model serves as an intermediary between the user's diverse queries and the specialized processing engines. It translates natural language queries into structured requests that the specialized engines can process efficiently, enabling adaptability without requiring the engines themselves to be complex or adaptable.
2Adaptability or versatility
If custom subroutines are added to address new inquiry scenarios, then the functionality improves, but the development and maintenance costs increase significantly
Solution Approach 1:
The language model provides a universal interface that handles diverse inquiry scenarios through natural language understanding rather than requiring custom subroutines for each scenario. The system architecture allows the same language model to work with multiple specialized engines, providing multi-functionality without duplicating code or creating maintenance burdens.
Solution Approach 2:
Instead of creating custom subroutines that duplicate logic across different inquiry scenarios, the system uses the language model to generate and interpret queries in a standardized format. This eliminates the need for multiple copies of similar processing logic, reducing development and maintenance costs while maintaining functionality.
3Productivity
If the engine requires specific input formats, then the processing efficiency improves, but the ease of operation deteriorates as users must learn engine-specific formats
Solution Approach 1:
The language model acts as an intermediary that accepts diverse natural language input from users and translates it into the structured formats required by the specialized processing engines. This maintains processing efficiency by ensuring engines receive properly formatted requests while improving ease of operation by allowing users to speak naturally without learning engine-specific syntax.
Solution Approach 2:
The system changes the parameter of input format flexibility by using the language model to adapt various natural language expressions into standardized query structures. This allows the system to maintain strict formatting requirements for engine processing while presenting a flexible, user-friendly interface that accepts diverse input formats.
Data Source
AI summary
A technique for interacting with map-related information integrates the use of a machine-trained language model. Upon submission of a query, the technique uses the machine-trained language model to assess at least one intent associated with the query. The technique then invokes an intent-specific processing flow to provide an output result. Each processing flow invokes the use of at least one processing engine to perform an engine-specific task, such as geocoding, route finding, or image retrieval. A processing flow can also call on the machine-trained language model one or more additional times. In some cases, the technique includes a feedback mechanism for soliciting additional information from a user.