Neural Network Response Hallucination Detection and Correction
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Solution Overview
Problem
Neural network models, such as large language models (LLMs), often produce hallucinations due to insufficient training data or input data errors, which existing methods like improving data quality or validation fail to completely prevent.
Innovation Solution
An electronic apparatus and method to detect hallucinations by assessing responses based on context within prompts, modifying responses to remove errors, and providing feedback for neural network model retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network models are trained with more data or improved data quality, then model performance improves, but hallucinations cannot be completely prevented
Solution Approach 1:
The patent applies preliminary action by detecting hallucinations before they affect the final output. The system assesses the response against the context from the prompt in advance, identifying hallucinations during the inference process rather than after training. This allows the system to prevent harmful outputs by catching issues early in the generation process.
Solution Approach 2:
The patent implements feedback by creating a mechanism that evaluates the response against the original prompt context and provides assessment results that can be used to modify or reject hallucinated outputs. The feedback loop assesses whether the response contains information not supported by the prompt context, enabling continuous improvement and correction of hallucinations in real-time.
2Reliability
If validation of input data is increased, then data quality improves, but processing time and complexity increase
Solution Approach 1:
The patent applies partial action by validating only the critical aspect of data quality - whether the response contains hallucinations relative to the prompt context - rather than performing comprehensive validation of all input data. This selective validation approach maintains reliability for the most important function (preventing hallucinations) while reducing overall processing time and computational complexity.
3Measurement precision
If hallucination detection is performed by assessing response against context, then detection accuracy improves, but computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the hallucination detection task into discrete assessable elements. Instead of analyzing the entire response at once, the system evaluates specific aspects of the response against the prompt context, breaking down the complex detection problem into manageable assessment units that can be processed more efficiently with reduced computational resources.
Data Source
AI summary
A method of removing a hallucination in a result of inference by a neural network model may include obtaining a response of a neural network model based on a prompt provided to the neural network model; determining, based on a context comprised in the prompt, whether a hallucination has occurred in the response; and based on determining that the hallucination has occurred, modifying the response and outputting the modified response.