Context-Aware Query Rewrite and Intent Routing for Low-Latency Search
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Solution Overview
Problem
Current prompt engineering techniques for machine learning models, particularly in natural language processing, face challenges such as high latency, computational costs, and inflexibility due to sequential query rewriting and the need for extensive training data, limiting their effectiveness in generating accurate and relevant search results.
Innovation Solution
An AI system employs a recursive summary technique to generate customized queries by summarizing context information from multiple queries within a session, using a combination of machine learning and rule-based logic to detect intent and route queries to appropriate models, reducing the need for extensive training data and enhancing accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential query rewriting is used to expand search queries with additional terms, then retrieval effectiveness is improved, but latency and computational costs increase
Solution Approach 1:
The query rewriting process is segmented into multiple specialized models, each handling specific aspects of query expansion. This allows parallel processing of different rewriting tasks simultaneously, reducing overall latency while maintaining comprehensive query enhancement through distributed computational work
Solution Approach 2:
Multiple query rewriting models are merged into a unified ensemble system that processes queries collectively. The ensemble combines results from various models to produce a comprehensive expanded query, achieving both high retrieval effectiveness through diverse perspective integration and improved efficiency through coordinated processing
2Reliability
If multiple models are used for query rewriting to improve accuracy, then retrieval quality is enhanced, but device complexity increases
Solution Approach 1:
The system employs a universal query rewriting framework that can accommodate multiple specialized models through a common interface and processing pipeline. This multi-functional architecture allows different models to serve various rewriting purposes while sharing infrastructure components, reducing overall system complexity despite using multiple models
Solution Approach 2:
An intermediary orchestration layer is introduced to manage multiple query rewriting models. This mediator handles model selection, result aggregation, and conflict resolution, simplifying the complexity of coordinating multiple models by providing a standardized interface that abstracts away the underlying complexity from the overall system
3Measurement precision
If extensive training data is used to train query rewriting models, then model performance is improved, but the need for extensive training data increases computational costs and time
Solution Approach 1:
Query rewriting models are pre-trained on comprehensive datasets and then fine-tuned on specific domain data. This preliminary action of pre-training allows the models to achieve good baseline performance without requiring extensive domain-specific training time, while still maintaining high accuracy through subsequent targeted fine-tuning
Solution Approach 2:
The system uses parameter-efficient fine-tuning techniques that modify only specific parameters of pre-trained models rather than retraining all parameters. This approach maintains model performance accuracy by preserving learned representations while reducing training time and computational costs through selective parameter updates
Data Source
AI summary
Artificial intelligence techniques for query management are described. A method comprises generating, by a context detection module, context information for a first query comprising natural language information to request a result from one of a plurality of machine learning models, modifying, by a query modification module, the first query based the context information to form a first modified query, determining, by an intent module, an intent type for the first modified query, selecting, by a routing module, a machine learning model from the plurality of machine learning models based on the intent type, and routing, by the routing module, the first modified query to the selected machine learning model. Other embodiments are described and claimed.