Adversarial Q&A Model Generation for Challenging Training Data
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Solution Overview
Problem
Existing question-answer models require large amounts of learning data and manual construction, and performance improvement is limited despite additional data, necessitating new methods to generate diverse and unguessable question-answer datasets.
Innovation Solution
A method and apparatus using adversarial learning to automatically generate question-answer pairs by sampling latent variables, controlling constraints, and increasing loss to create datasets that challenge the current models, involving operations like hidden representation generation, attention-based importance calculation, and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual construction of learning data is used, then data quality is high, but time consumption and cost increase significantly
Solution Approach 1:
The system uses self-service by having the model generate its own training data through adversarial learning. The question-answer model automatically creates challenging question-answer pairs by sampling latent variables and generating answers that the model itself cannot correctly predict, eliminating the need for manual data construction while maintaining high data quality through adversarial challenges.
Solution Approach 2:
The patent creates synthetic copies of training data by generating artificial question-answer pairs through the adversarial learning process. The system copies the structure of real question-answer data but fills it with generated content that challenges the model's current capabilities, providing high-quality training data without manual copying or annotation.
2Quantity of substance
If more data is used for learning, then training volume increases, but model performance improvement is limited
Solution Approach 1:
The system changes the parameters of the training data by generating question-answer pairs with specific adversarial properties. Instead of simply increasing data volume with random data, the system modifies data parameters to create challenging examples where the latent variables are sampled to maximize the model's loss function, ensuring each added data point actively improves model performance.
Solution Approach 2:
The patent converts the harmful effect of the model's current limitations into beneficial training data. By sampling latent variables that cause the model to fail (increasing loss), the system transforms these failures into valuable learning examples. The adversarial process converts the model's weaknesses into strengths, creating training data that specifically targets and improves areas where the model currently performs poorly.
3Reliability
If adversarial learning is used to generate challenging data, then model performance improves, but data generation complexity increases
Solution Approach 1:
The patent merges multiple functions into a unified adversarial learning system. The question-answer model, data generation mechanism, and performance evaluation are combined into a single integrated system. The model generates training data while simultaneously learning from it, and the same system evaluates performance, eliminating the need for separate complex subsystems and reducing overall system complexity despite the advanced learning technique.
Data Source
AI summary
A method of generating a question-answer learning model through adversarial learning may include: sampling a latent variable based on constraints in an input passage; generating an answer based on the latent variable; generating a question based on the answer; and machine-learning the question-answer learning model using a dataset of the generated question and answer, wherein the constraints are controlled so that the latent variable is present in a data manifold while increasing a loss of the question-answer learning model.