Interactive Interface for Low-Complexity Multimedia Model Evaluation
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Solution Overview
Problem
Evaluating the understanding capability of a machine learning model for multimedia content is complex and costly due to the need for users to manually upload content and set prompt information and model parameters, which can lead to unreasonable settings affecting the parsing effect and increasing operational complexity.
Innovation Solution
An interactive interface is provided with first interactive objects associated with multimedia content, prompt information, and model parameters, allowing users to select and send these to the machine learning model for evaluation, with the result being presented back to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually upload multimedia content and set prompt information and model parameters, then the model can execute the target task, but the operational complexity and cost increase
Solution Approach 1:
The system pre-configures prompt information and model parameters before the user needs to evaluate the model. These pre-set configurations are automatically applied when the user selects multimedia content for evaluation, eliminating the need for users to manually set these parameters and thereby reducing operational complexity
Solution Approach 2:
The system automatically handles the configuration and execution processes without requiring extensive user intervention. When users select multimedia content, the system automatically retrieves pre-configured prompt information and model parameters, processes the evaluation, and presents results, making the system serve itself rather than requiring complex manual setup
2Productivity
If users manually set prompt information and model parameters, then the model can be configured for specific tasks, but the time required for evaluation increases
Solution Approach 1:
Prompt information and model parameters are pre-configured and stored in the system before evaluation is needed. When users select multimedia content for evaluation, the system automatically retrieves and applies the pre-set parameters, eliminating the time-consuming process of manual parameter configuration and significantly speeding up the evaluation process
3Ease of operation
If pre-configured prompt information and model parameters are used, then the evaluation process is simplified, but the flexibility to customize may be reduced
Solution Approach 1:
The system provides a dynamic configuration mechanism that allows users to adjust pre-set prompt information and model parameters according to their specific needs. The interface enables users to modify parameters while maintaining the benefit of pre-configured defaults, thus preserving both operational simplicity and customization flexibility
Solution Approach 2:
The system allows users to change parameter values from the pre-configured defaults according to their specific evaluation needs. Users can adjust prompt information and model parameters within reasonable ranges while maintaining the simplified evaluation process, achieving both ease of operation and adaptability
Data Source
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AI summary
According to embodiments of the present disclosure, there is provided a method, an apparatus, a device and a storage medium for interaction. The method comprises: presenting an interactive interface, wherein the interactive interface comprises at least one first interactive object; in response to detecting a selection of a first interactive object in the at least one first interactive object, presenting the selected first interactive object, the prompt information and model parameter associated with the selected first interactive object in the interactive interface; in response to detecting a sending instruction for the selected first interactive object, sending the multimedia content, the prompt information and the model parameter associated with the selected first interactive object to the target machine learning model; and in response to receiving an execution result for the target task from the target machine learning model, presenting the execution result in the interactive interface. Thereby, a user can intuitively know the capability of the machine learning model at a relatively low operation cost.