ML Output Filtering for Hallucinated Feedback Detection
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Solution Overview
Problem
Machine learning models, particularly large language models (LLMs), often hallucinate and receive misleading user feedback, leading to poor performance and the need for improved methods to automatically check and filter user feedback and output for accuracy.
Innovation Solution
A method involving a rule-based system and machine learning models, such as autoencoders and masked language models, to detect and correct errors in user feedback and output, using a filter to generate corrected results for further processing and user review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are trained with user feedback, then the model's learning capability is improved, but the model performance deteriorates when receiving misleading or hallucinated feedback
Solution Approach 1:
The patent introduces an intermediary filtering system between user feedback and the machine learning model training process. This filter automatically detects and removes misleading or hallucinated feedback before it reaches the model, allowing the model to learn from quality feedback while being protected from harmful inputs. The intermediary layer resolves the contradiction by enabling continuous learning while maintaining performance reliability.
Solution Approach 2:
The patent implements a feedback loop where the filtering system continuously monitors and evaluates user feedback quality, using detection mechanisms to identify problematic patterns. This feedback mechanism allows the system to adaptively improve its filtering capabilities while ensuring only high-quality feedback reaches the training process, thus maintaining both learning capability and model performance.
2Measurement precision
If a filtering system is implemented to check user feedback, then the accuracy of training data is improved, but the processing time and system complexity increase
Solution Approach 1:
The patent divides the filtering system into multiple specialized components, each responsible for specific aspects of feedback validation. This segmentation allows the system to process different types of feedback through appropriate specialized filters, improving detection accuracy while managing complexity through modular design. Each segment handles specific error patterns, making the overall system more efficient and maintainable.
Solution Approach 2:
The patent implements a multi-layer filtering approach where not all feedback undergoes the complete filtering process. Instead, basic filtering is applied to all feedback, with more intensive processing reserved for suspicious cases. This partial action strategy maintains high accuracy for critical feedback while reducing overall processing time and computational resources required.
3Manufacturing precision
If automatic error detection and correction is applied to user feedback, then the quality of training data is improved, but the processing speed decreases
Solution Approach 1:
The patent applies preliminary filtering and basic error detection to user feedback before it enters the main processing pipeline. By performing initial quality checks and removing obviously problematic feedback early in the process, the system reduces the workload for subsequent detailed analysis stages, thereby maintaining high training data quality while preserving overall processing speed.
Solution Approach 2:
The patent implements a fast-track processing mode for feedback that passes initial quality checks. Feedback that appears valid and consistent with expected patterns is quickly routed through streamlined processing, while only suspicious cases undergo comprehensive error detection and correction. This skipping approach maintains high processing speed for the majority of feedback while ensuring thorough validation when needed.
Data Source
AI summary
A computer implemented method for filtering user feedback and/or output of a machine learning model, comprising: providing a filter for filtering user feedback and/or output of a machine learning model; receiving user feedback and/or output of the machine learning model; filtering the user feedback and/or the output with the filter and determining a filtering result, wherein the filtering result comprises at least a detected error; providing the filtering result for further processing.