Corpus Interaction Using Semantic Dependency Updates
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Solution Overview
Problem
Existing methods for editing and annotating corpus data, such as news articles and academic papers, are complex and prone to errors, leading to reduced quality and efficiency in generating corpus data for applications like large language model training.
Innovation Solution
An interaction method that updates first and second corpus contents with a semantic dependency relationship using a designated large model to automatically determine the user's intent, reducing interaction complexity and improving data quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex interaction operations are used for editing and annotating corpus data, then the completeness of data coverage can be improved, but the operation complexity and error rate increase
Solution Approach 1:
The system enables self-service by allowing the corpus data to automatically trigger updates of semantically dependent content without requiring manual intervention. When users interact with the first corpus content, the system automatically identifies and updates the second corpus content based on semantic dependency relationships, eliminating the need for complex manual operations while maintaining data completeness and quality.
Solution Approach 2:
The system introduces an intermediary mechanism in the form of semantic dependency relationships that mediate between user interactions and corpus data updates. This intermediary automatically determines which content needs updating based on semantic relationships, simplifying the user interface while ensuring comprehensive and accurate updates across related corpus data.
2Productivity
If manual editing operations are performed on corpus data, then specific content can be modified, but the efficiency and consistency of updates across related content decrease
Solution Approach 1:
The system implements feedback by automatically identifying semantic dependency relationships between corpus contents and triggering updates to related content when the first corpus content is modified. This feedback mechanism ensures that updates propagate consistently across all semantically related content, maintaining semantic consistency while significantly improving update efficiency compared to manual methods.
Solution Approach 2:
The system performs preliminary action by pre-establishing semantic dependency relationships between corpus contents before user interactions occur. This allows the system to automatically determine which content needs updating based on pre-analyzed semantic relationships, enabling efficient and consistent updates across related content without requiring manual analysis during the editing process.
3Reliability
If comprehensive updates of semantically dependent content are performed, then the semantic integrity of corpus data is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-establishing semantic dependency relationships between corpus contents before user interactions occur. This allows the system to quickly identify and update only the necessary semantically dependent content when modifications are made, maintaining semantic integrity while minimizing processing time and computational resource requirements.
Data Source
AI summary
An interaction method, an electronic device, and a storage medium are provided, which relate to the field of artificial intelligence technologies, and in particular to the fields such as deep learning, large models, and intelligent question answering. The interaction method includes: displaying a first corpus content in received corpus data; in response to an interaction operation performed by a target object on the first corpus content, updating the first corpus content and a second corpus content having a semantic dependency relationship with the first corpus content in the corpus data, to obtain target corpus data; and determining a feedback information related to a demand intention of the target object based on the target corpus data.