Machine Learning Label Generation via User Feedback
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Solution Overview
Problem
Current machine learning model training relies heavily on manual annotation of labeled data, which is time-consuming and inefficient, and existing methods do not effectively leverage user feedback for real-time data generation and model updates.
Innovation Solution
A system that utilizes both implicit and explicit user feedback to generate labeled data in near real-time, employing label generators to aggregate feedback and reduce the need for manual annotation, while ensuring user privacy and compliance with regulations through permission-based processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation is used to generate labeled data for machine learning training, then data accuracy and reliability are improved, but time consumption and processing efficiency deteriorate
Solution Approach 1:
The system implements a feedback loop where user interactions with the machine learning model are collected and used to automatically generate labeled training data. This feedback mechanism transforms operational data into training samples, enabling continuous model improvement without manual annotation while maintaining reliability through real user behavior data.
Solution Approach 2:
The machine learning system performs self-training by automatically generating its own training data from user feedback and operational data. The system self-updates its models using the generated labeled data, eliminating the need for external manual annotation processes while maintaining data quality through structured feedback collection.
2Reliability
If manual annotation processes are used to create training data, then data quality is improved, but productivity and update speed deteriorate
Solution Approach 1:
The system collects and stores user feedback data in real-time during normal operations, preparing labeled training data in advance before it is needed for model retraining. This preliminary data collection and labeling action enables rapid model updates when triggered, as the labeled data is already prepared and available for immediate use.
Solution Approach 2:
The system maintains continuous collection of user feedback data during normal model operation, ensuring that labeled training data generation is an ongoing process rather than a batch operation. This continuous data accumulation enables frequent and rapid model updates while maintaining data quality through consistent feedback collection across all user interactions.
3Productivity
If user feedback is collected and processed in real-time, then model update speed is improved, but system complexity and processing requirements worsen
Solution Approach 1:
The system segments the data processing pipeline into distinct modular components: feedback collection module, data preprocessing module, labeling module, and model training module. Each component handles a specific aspect of the data flow, allowing independent optimization and maintenance while reducing overall system complexity through clear separation of concerns.
Solution Approach 2:
The system introduces intermediate data processing layers that mediate between raw user feedback and the machine learning model training process. These intermediaries include data cleaning, feature extraction, and validation layers that simplify the complexity by handling preprocessing tasks separately, allowing the core training algorithm to focus on learning from already-processed data.
Data Source
AI summary
Techniques for performing centralized unsuperivised learning in a multi-domain system are described. A user may request labeled data for an ML task, where the request includes a prompt for obtaining relevant explicit user feedback. The system may use the prompt to collect explicit user feedback for relevant runtime user inputs. After a duration of time (in the user's request for labeled data) has elapsed, the system determines whether collected user feedback indicates processing of the user input was defective and, if so, determines a cause of the defective processing. The system then uses one or more label generators to generate labeled data using the collected user feedback, whether the processing was defective, and the determined defect cause.