Synthetic Data Generation with Embedding Similarity Evaluation
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Solution Overview
Problem
The challenge in managing the quantity of unstructured data required for training deep learning models, particularly in generating synthetic data that aligns with user intent and evaluating the quality of such data for effective artificial intelligence model training and evaluation.
Innovation Solution
A computing device configured to receive user queries, determine constraints, generate structured queries for both databases and generative models, and provide synthetic data while evaluating its similarity to imported data, enabling the training and evaluation of artificial intelligence models using synthetic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If synthetic data is generated using generative models, then data quantity for training is improved, but data quality and alignment with user intent may deteriorate
Solution Approach 1:
The system implements feedback loops where generation results are evaluated against constraints and user intent, and this evaluation information is fed back to adjust subsequent generation processes. The evaluation module assesses generated synthetic data quality and provides feedback to improve alignment with user requirements.
Solution Approach 2:
The system performs preliminary determination of constraints and requirements before generating synthetic data. By establishing evaluation criteria and constraints in advance, the generative model can be guided to produce data that better aligns with user intent from the outset.
2Manufacturing precision
If multiple processing steps are implemented for data generation and evaluation, then data quality is improved, but system complexity deteriorates
Solution Approach 1:
The system merges multiple functions into integrated modules: the constraint determination module combines requirement analysis and constraint formulation, the generation module integrates the generative model with evaluation capabilities, and the feedback mechanism combines quality assessment with parameter adjustment. This modular integration reduces overall system complexity while maintaining data quality.
3Productivity
If synthetic data generation is used, then training efficiency is improved, but evaluation accuracy may deteriorate
Solution Approach 1:
The system dynamically adjusts generation parameters based on evaluation results and feedback. By changing parameters such as generation constraints, model configurations, and evaluation criteria, the system optimizes both training efficiency and evaluation accuracy, ensuring synthetic data maintains sufficient fidelity for accurate model evaluation.
Data Source
AI summary
A device, for generating synthetic data, configured to: receive a first input query requesting data generation; determine a constraint related to synthetic data based on the first input query; obtain a first structured query processed in a predetermined manner suitable for a database based on the constraint; provide the first structured query to the database to obtain imported data; obtain a second structured query processed in a predetermined manner suitable for a generative model based on the constraint; provide the second structured query to the generative model; obtain synthetic data from the generative model; determine similarity between the imported data and the synthetic data by calculating a distance in embedding space between an embedded feature of the imported data and an embedded feature of synthetic data; and provide output data comprising the imported data, the synthetic data, and similarity information.