Response Generation with Candidate Attitudes and Prompt Automation
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Solution Overview
Problem
Existing machine learning-based response generation processes require substantial manual operations, including constructing prompts, which are inefficient and cumbersome for users.
Innovation Solution
A method and apparatus that utilize a machine learning model to generate responses by providing candidate attitudes, allowing users to select an attitude, and automatically generate a response based on the first data object's content and the selected attitude, with optional refinement through interaction requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual prompt construction is used for machine learning-based response generation, then response accuracy can be controlled, but the operation complexity and time consumption increase substantially
Solution Approach 1:
The system automatically generates response prompts using the machine learning model without requiring manual prompt construction. The model self-services by generating both the prompt and response based on the input data object and selected attitude, eliminating the need for users to manually craft prompts while maintaining response quality
Solution Approach 2:
The system pre-provides multiple candidate attitudes for the user to select from before response generation. This preliminary action of presenting structured options simplifies the user's decision-making process and enables automatic prompt construction based on the selected attitude, reducing operational complexity
2Adaptability or versatility
If manual prompt construction is required, then response generation can be customized, but productivity decreases due to substantial manual operations
Solution Approach 1:
The system dynamically adjusts the response generation process based on user interactions. After generating an initial response, the system can refine it by providing candidate content items for replacement based on additional user input, allowing customization to evolve dynamically while maintaining high productivity
Solution Approach 2:
The system incorporates feedback loops where user selections of candidate attitudes and content items are used to iteratively refine the response. This feedback mechanism enables customized responses to be generated efficiently by learning from user preferences without requiring manual prompt construction at each step
3Productivity
If automatic response generation is implemented, then operational efficiency improves, but the level of automation may insufficiently reduce manual input requirements
Solution Approach 1:
The machine learning model automatically generates both the prompt and response content without requiring manual prompt construction. This self-service capability significantly reduces manual input requirements while maintaining operational efficiency, as the system handles the entire prompt engineering process autonomously based on the selected attitude
Data Source
AI summary
A method, an apparatus, a device, and a medium for generating a response are provided. In a method, a first data object is obtained. A plurality of candidate attitudes for a response to the first data object are provided. In response to receiving a first interaction request for a candidate attitude among the plurality of candidate attitudes, a second data object is generated as the response to the first data object, wherein second content of the second data object is determined based on first content of the first data object and the candidate attitude. In aid of the example embodiments of this disclosure, by providing a plurality of candidate attitudes, a user can easily select a desired attitude, and a response is automatically generated. Thereby, the complexity of user’s operation can be reduced, and a response is generated in a simpler and more efficient way.