Multi-Dimensional Truthfulness Correction in Text Feature Decoding
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Solution Overview
Problem
Existing text processing methods suffer from a hallucination phenomenon, leading to degraded truthfulness and low accuracy in the prediction dimension, resulting in inaccurate target text determination.
Innovation Solution
Perform feature encoding on a to-be-processed text to obtain an initial text feature, followed by truthfulness prediction in multiple dimensions. When the prediction indicates untruthfulness, a correction feature is applied to refine the initial text feature, and subsequent feature decoding generates a target text that is truthful in each dimension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feature encoding and feature decoding are directly performed on the to-be-processed text, then the text processing speed is maintained, but the truthfulness of the target text is degraded due to hallucination phenomenon
Solution Approach 1:
The patent performs truthfulness prediction on the initial text feature before proceeding to feature decoding. This preliminary action identifies potential hallucination issues in advance, allowing the system to generate correction features that address truthfulness problems before they manifest in the final target text, thereby resolving the contradiction between maintaining processing speed and improving truthfulness.
Solution Approach 2:
The patent introduces correction features as an intermediary element between the initial text feature and the target text. These correction features are generated based on truthfulness prediction results and are used to adjust the initial text feature, serving as a mediator that eliminates hallucination effects while preserving the overall text processing efficiency.
2Measurement precision
If truthfulness prediction and correction features are applied to improve target text accuracy, then text processing accuracy is enhanced, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies correction features selectively based on truthfulness prediction results. Rather than applying comprehensive corrections to all text features, the system performs partial action by generating correction features only for dimensions where hallucination is detected, thereby improving accuracy while minimizing additional processing time and computational overhead.
3Reliability
If multiple prediction dimensions are used to evaluate truthfulness, then the comprehensiveness of text evaluation is improved, but the computational burden and processing complexity increase
Solution Approach 1:
The patent segments the truthfulness evaluation into multiple independent prediction dimensions, where each dimension evaluates a specific aspect of text truthfulness. This segmentation allows the system to comprehensively evaluate text across different dimensions while maintaining manageable computational complexity by processing each dimension separately and combining results.
Data Source
AI summary
In a method, an initial text feature of a to-be-processed text is obtained based on feature encoding of the to-be-processed text. One or more truthfulness prediction results of the to-be-processed text in one or more prediction dimensions are obtained, the one or more truthfulness prediction results corresponding to one or more truthfulness predictions of logic of the to-be-processed text in the one or more prediction dimensions based on the initial text feature. For each prediction dimension, a correction feature of the initial text feature is obtained when the corresponding truthfulness prediction result in the respective prediction dimension indicates that the to-be-processed text is not truthful in the respective prediction dimension. A target text feature corresponding to the initial text feature is obtained based on the correction feature for each prediction dimension. A target text corresponding to the to-be-processed text is obtained based on feature decoding of the target text feature.