Personalized System Prompts for Preference-Aligned Language Output
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Solution Overview
Problem
Existing generative AI language models often generate outputs that are not sufficiently tailored to user or operator preferences, leading to irrelevant or unwanted responses.
Innovation Solution
A system and method that incorporates user-specific and operator-specific prompts to modify the system prompt for a machine-learning language model, ensuring that generated natural-language outputs align with both user and operator preferences, thereby improving relevance and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a standard system prompt is used without personalization, then the system operation is simple, but the output relevance to user preferences deteriorates
Solution Approach 1:
The system performs preliminary action by generating personalized system prompts in advance based on user preferences and operator goals before the actual language generation task. This pre-processing step creates customized prompts that are then used during inference, improving output relevance without adding complexity to the core generation process.
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between user/operator inputs and the language model. This intermediary processes and structures preferences into standardized prompt formats, enabling personalized outputs while keeping the language model itself unchanged and simple.
2Manufacturing precision
If user-specific and operator-specific prompts are integrated, then output relevance to preferences improves, but the processing time increases
Solution Approach 1:
The system segments the prompt construction process into distinct modules: user preference processing, operator goal processing, and prompt assembly. This segmentation allows each component to be optimized independently and enables parallel processing of user and operator inputs, reducing overall processing time while maintaining high output relevance.
3Reliability
If personalized system prompts are generated, then user satisfaction increases, but the computational resources required increase
Solution Approach 1:
The system uses copying by creating template-based prompt structures that can be reused across different users and scenarios. Instead of generating entirely new prompts each time, the system copies and adapts proven prompt templates with user-specific parameters, significantly reducing computational resources while maintaining high user satisfaction through personalization.
Data Source
AI summary
A method of automatic pre-prompt generation includes receiving, by a user device, an indication of at least one user preference, where the at least one user preference indicative of at least one first characteristic preferred by a user of natural-language outputs generated by a machine-learning language model. The method further includes, by a server, receiving a natural-language text prompt provided by the user, receiving the at least one user preference from the user device, modifying a system prompt based on the received at least one user preference and the at least one first operator preference, providing the modified system prompt as an initial input to the machine-learning language model, providing the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output after providing the modified system prompt, and transmitting the natural-language text output to the user device.