Multi-Model Query Routing and Synthesis for Coherent ML Answers
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Solution Overview
Problem
Existing machine learning systems struggle to provide comprehensive and coherent answers to complex, multi-modal queries that require integration across multiple knowledge domains and modalities, often resulting in inefficient and time-consuming manual synthesis of responses.
Innovation Solution
A framework that integrates multiple machine learning systems to process queries through stages of decode, route, map, and execute, synthesizing answers while ensuring compliance with rules and regulations, and leveraging local data for context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning systems are used to process complex queries across multiple knowledge domains, then the comprehensiveness and accuracy of responses is improved, but the system complexity and time required for synthesis increases
Solution Approach 1:
The system segments the query processing into distinct stages: decoding the query to identify knowledge domains and modalities, routing to appropriate ML systems, mapping responses to the original query structure, and executing synthesis. This segmentation manages complexity by organizing multiple ML systems into a structured workflow rather than a monolithic system.
Solution Approach 2:
The framework introduces intermediary components including a query decoder that translates queries into routing instructions, and a response synthesizer that integrates outputs from multiple ML systems. These intermediaries manage the complexity of coordinating multiple specialized systems without requiring direct integration between each pair of systems.
2Reliability
If multiple machine learning systems are used to process complex queries, then the comprehensiveness of responses is improved, but the time required for processing increases
Solution Approach 1:
The query decoder performs preliminary analysis of the incoming query to identify required knowledge domains, modalities, and appropriate ML systems before routing. This preliminary action prevents unnecessary processing by determining in advance which specialized systems need to be engaged, reducing overall processing time while maintaining comprehensiveness.
3Measurement precision
If manual synthesis of responses from multiple ML systems is performed, then accuracy can be ensured, but efficiency and speed decrease
Solution Approach 1:
The framework implements automated response synthesis that performs the integration and coordination functions previously requiring manual intervention. The synthesizer automatically integrates outputs from multiple ML systems, reconciles conflicting information, and produces coherent responses without human intervention, thereby maintaining accuracy while dramatically improving efficiency.
4Measurement precision
If specialized ML systems are used for specific knowledge domains, then expertise and accuracy in those domains is improved, but the difficulty of integrating multiple systems increases
Solution Approach 1:
The query decoder and response synthesizer serve as universal components that handle multiple knowledge domains and modalities through standardized interfaces. Rather than requiring custom integration logic for each pair of specialized systems, these universal intermediaries provide a common framework that simplifies the integration of diverse domain-specific ML systems.
Data Source
AI summary
An integration framework combines multiple machine learning (ML) models to provide an aggregated answer to a user query. The user query may be disambiguated and broken down into one or more simplified queries. These simplified queries are then analyzed to determine which of the ML models should be used to answer the queries, and an order in which the queries should be processed by the selected ML models is established. The queries are then processed through the selected ML models, and the responses are compiled into a final coherent answer. The answer may be checked for compliance with any relevant rules before being presented to the user and/or stored for further use. Other embodiments may be described and/or claimed.