Search Ranking Distillation for Accurate Low-Latency Content Retrieval
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Solution Overview
Problem
Existing search applications in connection network systems face challenges in accurately measuring relevance and engagement of content items, relying on traditional programming and ML models that are resource-intensive, slow, and require substantial training data, leading to inefficiencies in scalability and latency.
Innovation Solution
Implementing a training technique using knowledge distillation to create a smaller, efficient student model from a teacher model, leveraging generative AI to generate training data and adapt to changing objectives, and deploying it in a layered architecture for rapid inferencing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ML models are used for search ranking, then measurement precision of relevance and engagement is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent creates simplified copy versions of complex ML models through knowledge distillation. A student model is trained to replicate the predictions of a teacher model, producing a lighter version that maintains accuracy while reducing computational complexity and resource requirements for deployment in search ranking systems.
Solution Approach 2:
The patent transforms the model representation by changing parameters from complex neural network architectures to simpler structures with fewer parameters. This parameter transformation through knowledge distillation reduces device complexity while preserving the essential predictive capabilities needed for accurate relevance measurement.
2Measurement precision
If traditional ML models are used for search ranking, then measurement precision of relevance and engagement is improved, but productivity and processing speed decrease
Solution Approach 1:
The student model serves as a fast copy of the teacher model, designed to replicate its predictive accuracy while executing significantly faster. This copied version enables high-throughput processing of search queries without sacrificing relevance measurement precision, directly improving system productivity.
3Measurement precision
If traditional ML models are used for search ranking, then measurement precision of engagement is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary training of the student model using synthetically generated engagement data created by the generative AI system. This preliminary action with artificially generated data prepares the model for deployment without requiring extensive collection of real user engagement data, significantly reducing the time loss associated with data gathering and model training.
Solution Approach 2:
The generative AI system serves itself by automatically generating the training data needed to train the student model. This self-service capability eliminates the need for external data collection processes, allowing the system to bootstrap its training data requirements and reduce time loss independently.
4Adaptability or versatility
If generative AI is used to generate training data, then adaptability to changing objectives is improved, but device complexity increases
Solution Approach 1:
The generative AI system acts as an intermediary between changing objectives and the student model training process. When objectives change, the generative AI translates these new requirements into appropriate training data and objectives for the student model, mediating the complexity of adaptation while maintaining system versatility.
Data Source
AI summary
Artificial intelligence (AI) techniques for connection networking are described. A method comprises generating a first training prompt based on a set of guidelines for a network service of a connection network system, the guidelines defining an objective for the network service, sending the first training prompt and a first set of training datapoints from a first training dataset to a first generative AI model, a training datapoint from the first set of training datapoints comprising a content item, receiving a second set of training datapoints for a second training dataset from the first generative AI model, wherein a training datapoint of the second training dataset comprises a first label for the content item generated by the first generative AI model based on the objective, and training a second generative AI model using the second set of training datapoints based on the objective. Other embodiments are described and claimed.