Ground Truth Weighting for QA Systems
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Solution Overview
Problem
The collection of ground truth for QA systems is often time-consuming and costly, particularly when adapting to new domains or customers, as it typically requires engagement from Subject Matter Experts or collaborative models involving multiple stakeholders.
Innovation Solution
A method that weights ground truth instances from different sources based on their level of trust, allowing for the adjustment of loss functions and instance duplication in machine learning tasks, incorporating validation from Subject Matter Experts, QA system developers, and crowdsource users to prioritize more trusted answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ground truth is collected from multiple sources with varying trust levels, then the quality and confidence of answers improve, but the complexity of managing and weighting different sources increases
Solution Approach 1:
The patent assigns different trust weights to different ground truth sources based on their local characteristics and reliability. Each source (SMEs, developers, crowdsource users) receives a specific weight reflecting its trust level, allowing the system to differentiate quality across sources rather than treating all sources equally. This resolves the contradiction by systematically managing source diversity through localized quality assessment.
Solution Approach 2:
The patent introduces trust weights as a parameter to quantify and manage the reliability of different ground truth sources. By converting qualitative trust assessments into quantitative weights that can be applied during training, the system simplifies the management of multiple sources. This parameter-based approach transforms the complex qualitative judgment of source reliability into a manageable numerical system.
2Reliability
If ground truth collection relies on Subject Matter Experts, then the accuracy and trustworthiness of answers improve, but the time and cost required for collection increase
Solution Approach 1:
The patent applies partial action by using SMEs only for the most critical ground truth instances where their expertise provides the greatest value. Rather than requiring SME validation for all ground truth, the system strategically applies SME validation to high-impact cases while using less resource-intensive sources for other instances, optimizing the time-cost versus reliability tradeoff.
Solution Approach 2:
The patent introduces trust weights as an intermediary mechanism that mediates between expensive SME-validated ground truth and cheaper alternative sources. The weighting system acts as a mediator that allows the model to learn from multiple sources while automatically prioritizing SME-validated instances during training, reducing the need for extensive SME involvement while maintaining reliability.
3Measurement precision
If ground truth instances are weighted by trust level, then the model learns more accurate patterns, but the computational complexity of training increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating and assigning trust weights to different ground truth sources before the actual training process. This upfront classification and weighting of sources based on their reliability eliminates the need for complex real-time calculations during training, reducing computational complexity while maintaining learning accuracy. The heavy lifting of trust assessment is done beforehand.
Solution Approach 2:
The patent implements dynamic weighting where the influence of different ground truth sources is adjusted based on their trust levels during the training process. The loss function dynamically incorporates trust weights to modulate the learning signal from different sources, allowing the model to adaptively focus on more reliable instances while still learning from less reliable ones, balancing accuracy and computational efficiency.
Data Source
AI summary
A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighted differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.