Adaptive Query Routing for Natural Language Generators
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Solution Overview
Problem
Generative AI technologies, such as large language models, consume significant computing resources during both training and inference phases, leading to inefficiencies in resource utilization.
Innovation Solution
Implement adaptive query routing techniques that assess query difficulty and route less difficult queries to less resource-intensive natural language generators, while more difficult queries are routed to more resource-intensive models, using machine learning models to determine query difficulty and minimize loss functions for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more resource-intensive natural language generator models are used to process all queries, then response quality is maintained or improved, but computing resource consumption increases significantly
Solution Approach 1:
The patent applies local quality by differentiating the quality of service based on query difficulty. Easy queries are served by less resource-intensive models while difficult queries are served by more capable models. This creates a non-uniform allocation of computational resources where each query receives the appropriate level of processing power needed for its specific requirements, thereby reducing overall resource consumption while maintaining response quality for difficult queries.
Solution Approach 2:
The patent implements dynamics through dynamic routing decisions based on query difficulty assessment. The system continuously evaluates query characteristics and dynamically assigns queries to appropriate models in real-time. This dynamic allocation allows the system to adapt resource distribution to actual query needs, preventing over-provisioning of resources for easy queries while ensuring adequate resources for difficult queries.
2Use of energy by moving object
If less resource-intensive natural language generator models are used to process all queries, then computing resource consumption is reduced, but response quality deteriorates for difficult queries
Solution Approach 1:
The patent resolves this contradiction by applying local quality differently - instead of uniformly applying low-resource models to all queries, it selectively applies appropriate model resources based on local query characteristics. Easy queries receive efficient low-resource model processing while difficult queries receive enhanced model processing, creating a differentiated service model that optimizes both resource consumption and quality where needed.
Solution Approach 2:
The system dynamically adjusts the resource allocation strategy based on real-time query difficulty assessment. Rather than static resource allocation, the system continuously monitors query characteristics and adapts model assignment accordingly, ensuring that resource-intensive models are activated only when query difficulty warrants it, thus optimizing the balance between resource consumption and response quality.
3Device complexity
If a single uniform model is used for all queries, then system complexity is reduced, but resource utilization efficiency decreases
Solution Approach 1:
The patent applies segmentation by dividing the query processing workload into distinct segments based on difficulty level. The system segments queries into easy, medium, and difficult categories and routes them to appropriately sized models. This segmentation strategy improves resource utilization efficiency by matching model capacity to query requirements, while the added routing infrastructure represents a manageable increase in system complexity.
Solution Approach 2:
The system introduces dynamic routing mechanisms that continuously assess query characteristics and adjust model assignment in real-time. This dynamic approach enables the system to optimize resource utilization efficiency by adapting to varying query patterns, while the routing logic provides a structured method for managing the increased system complexity through automated decision-making.
Data Source
AI summary
Natural language generators (NLGs), including large language models, are powerful technologies that are in widespread use. However, typically, as NLGs become more powerful and sophisticated, their correspondingly increased complexity requires substantial processing resources. The present disclosure provides automated techniques for dynamically routing queries between at least two NLGs based on an assessment of query difficulty. Less difficult queries can be routed to a less resource intensive NLG, while more difficult queries are routed to a more sophisticated, but more resource intensive NLG. Routing less difficult queries to a less resource intensive model can thus conserve computing resources, while providing little to no drop in response quality, and in some cases providing improved response quality.