Personalized Prompt Generation for LLM Customization
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Solution Overview
Problem
Large language models struggle to provide customized answers in personalized question-answering scenarios, leading to unsatisfactory results for users.
Innovation Solution
An information processing method that involves obtaining a user's input request, determining relevant data from a target data source based on user information, generating prompt information, and sending the input request and prompt information to a target model for language processing to produce customized feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large language model processes user requests without customized prompt information, then the processing speed is fast and the system complexity is low, but the output data is not customized enough and fails to satisfy users in personalized scenarios
Solution Approach 1:
The system performs preliminary actions by determining user information and generating customized prompt information before the target model processes the input request. This preparation step includes obtaining user information, determining relevant data from data sources, and constructing personalized prompts that are then fed to the model along with the user's request, ensuring customized output without requiring changes to the model itself
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between the user's input request and the target model. This intermediary determines user information, retrieves relevant data from data sources, generates customized prompt information, and combines these elements before submitting to the model. This mediator layer enables customization without modifying the underlying model architecture
2Reliability
If the model processes requests without personalized data, then the processing efficiency is high, but the satisfaction of users in personalized question-answering scenarios is low
Solution Approach 1:
The system segments the processing workflow into distinct functional modules: obtaining user information, determining relevant data from data sources based on the input request and user information, generating customized prompt information, and finally submitting to the target model. This segmentation allows each module to be optimized independently, maintaining efficiency while enabling personalization
3Adaptability or versatility
If customized prompt information is generated and sent to the target model, then the customization of output data is improved, but the processing time increases
Solution Approach 1:
The system applies partial action by selectively determining user information and generating customized prompt information only when needed for personalized scenarios. The system evaluates the input request and user information to determine whether customization is necessary, applying the personalized processing only in relevant cases rather than universally, thus balancing customization benefits with processing time costs
Data Source
AI summary
An information processing method includes obtaining an input request of a user; based on a relationship between the input request and user information of the user, determining, from a target data source, at least first data in which the input request satisfies a relevant condition with the user information in a direction of the requested result, the target data source including data related to the user information; generating first prompt information based on the first data; and sending the input request and model prompt information to a target model for performing language processing on the input request based on the model prompt information and obtaining a feedback result corresponding to the input request in the direction of the requested result, the model prompt information including the first prompt information.