NL2LF Training Data Augmentation Against Forgetting and Bias
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language to logical form (NL2LF) models suffer from catastrophic forgetting and over-generalization during customization, leading to inaccurate predictions and biased learning when transitioning from cross-domain to custom datasets.
Innovation Solution
Implementing contrastive and alternative example augmentation techniques to generate expanded training data by revising natural language examples and modifying operators, addressing regression and bias issues in machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model is trained on custom training data to improve performance on specific tasks, then the model achieves better specialization and accuracy on custom datasets, but the model suffers from catastrophic forgetting and loses previously learned knowledge from general training data
Solution Approach 1:
The system performs preliminary action by generating contrastive examples before the model is trained on custom data. These contrastive examples are created by identifying syntax patterns in examples that the pre-built model gets wrong and revising the natural language to include the missing syntax. This preliminary preparation of training data with corrected syntax patterns helps prevent catastrophic forgetting by proactively addressing potential knowledge loss areas before custom training begins.
Solution Approach 2:
The system creates copies of training examples with modified operators to generate alternative examples. By copying existing examples and systematically modifying operators (e.g., changing comparison operators, logical operators), the system generates diverse training variations that reinforce previously learned patterns while adapting to custom domain requirements, thus preserving general knowledge during specialization.
2Adaptability or versatility
If a model is trained on custom training data to achieve better specialization, then the model improves performance on specific domains, but the model exhibits over-generalization and biased learning when transitioning from cross-domain to custom datasets
Solution Approach 1:
The system applies local quality by making different parts of the training data have different characteristics. Specifically, it generates contrastive examples with revised natural language syntax for areas where the model shows weaknesses, while generating alternative examples with modified operators for areas showing bias. This localized modification of training data quality ensures that each part of the training process addresses specific problem areas without introducing general bias.
Solution Approach 2:
The system changes parameters of training examples by modifying operators in the MRL (Meaning Representation Language). By systematically altering operators such as comparison operators, logical operators, and quantifiers in the generated examples, the system creates diverse training variations that prevent the model from over-generalizing and reduce learning bias while maintaining domain specialization.
Data Source
AI summary
Techniques are disclosed herein for addressing catastrophic forgetting and over-generalization while training a model to transform natural language to a logical form such as a meaning representation language. The techniques include accessing training data comprising natural language examples, augmenting the training data to generate expanded training data, training a machine learning model on the expanded training data, and providing the trained machine learning model. The augmenting includes (i) generating contrastive examples by revising natural language of examples identified to have caused regression during training of a machine learning model with the training data, (ii) generating alternative examples by modifying operators of examples identified within the training data that belong to a concept that exhibits bias, or (iii) a combination of (i) and (ii).