Benchmark Query Generation With Language Models for Category Matching
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Solution Overview
Problem
Existing systems face challenges in efficiently generating diverse and high-quality benchmark query sets for user search queries, particularly when supporting new categories, and accurately matching user queries to these sets, which is time-consuming and often requires manual effort.
Innovation Solution
A system uses a trained language model to generate benchmark queries based on category-specific information, applies evaluation criteria, and employs machine learning models to embed queries in a multi-dimensional space for efficient matching, reducing the need for grammar-based models and manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to create benchmark query sets, then the queries can be customized for specific categories, but the process is time-intensive and costly
Solution Approach 1:
The patent replaces manual mechanical processes of query creation with automated machine learning models. The system uses trained ML models to generate benchmark queries automatically, eliminating the need for human experts to manually craft queries while maintaining or improving accuracy through systematic learning from data.
Solution Approach 2:
The system enables self-service by allowing the benchmark query generation process to autonomously improve through continuous learning. The machine learning models automatically refine their query generation capabilities by learning from feedback and performance metrics without requiring constant manual intervention or retraining by experts.
2Reliability
If manual methods are used to create benchmark query sets, then expert knowledge can be incorporated, but the process is costly and slow
Solution Approach 1:
The patent substitutes manual expert processes with automated machine learning systems. The ML models are trained on data that captures expert knowledge patterns, allowing them to generate reliable benchmark queries automatically without requiring continuous human expert involvement, thus improving both speed and maintaining quality.
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on comprehensive datasets that encode expert knowledge before deployment. This upfront training phase captures domain expertise in the model parameters, enabling rapid and reliable query generation without needing experts present during the actual benchmark creation process.
3Ease of operation
If grammar-based models are used for query matching, then the matching process can be systematic, but the development is painstaking and requires deep language understanding
Solution Approach 1:
The patent replaces complex grammar-based matching systems with machine learning-based semantic matching. Instead of manually constructing grammatical rules and language models, the system uses trained ML models that automatically learn semantic relationships from data, simplifying the development process while improving matching effectiveness.
4Adaptability or versatility
If diverse benchmark queries are manually created, then category coverage can be improved, but the manual effort increases
Solution Approach 1:
The system achieves self-service by automatically generating diverse benchmark queries through machine learning models that can explore and discover relevant query patterns independently. The models autonomously create comprehensive query sets covering multiple categories and scenarios without requiring manual effort, while continuously improving through feedback loops.
Data Source
AI summary
A method of facilitating content selection includes generating benchmark queries for a particular category. Generating the benchmark queries includes applying a text prompt as input to a language model trained on a knowledge base. The text prompt requests search queries indicative of user interest in the particular category. The method also includes selecting, responsive to new search queries entered by users, content items associated with the particular category for delivery to client devices of the users. Selecting the content items includes determining whether the new search queries correspond to the particular category at least in part by comparing the new search queries to a query set that includes the benchmark queries.