LLM Personalization Using Supplemental Prompts and User Response Records
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models lack personalization and customization, generating the same output for different users regardless of their individual characteristics, such as age, education, or background.
Innovation Solution
A system that refines machine learning models by storing user responses to prompts, analyzing these responses to determine user attributes, and generating supplemental prompts to gather more data, ultimately training the model to customize outputs based on individual user characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large language model generates content for multiple users, then the model can serve a broad audience, but the output lacks personalization and customization for individual users
Solution Approach 1:
The patent segments the personalization process by separating the collection of user response data from the model training process. User responses to prompts are collected and stored separately, then used to refine the model in a distinct training phase. This allows the model to maintain its general functionality while adding personalized capabilities through incremental refinement based on individual user data.
Solution Approach 2:
The patent implements preliminary action by collecting and storing user response data before using it to refine the model. User interactions with prompts are captured and accumulated in advance, creating a dataset that can be used for subsequent model refinement. This preliminary data collection enables the model to be customized without requiring complete retraining from scratch.
2Adaptability or versatility
If the model is refined based on individual user responses, then personalized output is achieved, but the time and computational resources required increase
Solution Approach 1:
The patent applies partial action by refining the model only on the subset of data most relevant to personalization - specifically, user responses to prompts. Rather than retraining the entire model on all possible data, the system focuses computational efforts on the specific user interaction data that directly contributes to personalization, achieving customization with reduced time and resource requirements.
3Loss of information
If supplemental prompts are provided to gather user data, then more comprehensive user profiles are created, but the user interaction burden increases
Solution Approach 1:
The patent implements universality by designing prompts that serve multiple functions simultaneously. The same prompts used for general model interaction also serve to collect user response data for personalization. This multi-functionality allows the system to gather comprehensive user information without requiring separate, dedicated data collection interactions, thereby maintaining interface simplicity while reducing information loss.
Data Source
AI summary
Systems and methods for refining machine learning models based on user responses to prompts. Exemplary implementations may: store response records for individual users that include responses by individual ones of the users to prompts; provide supplemental prompts to a first user; receive user input from the first user indication responses to the supplemental prompts; obtain the first response record for the first user from storage; for the first user, refine a first machine learning model based on responses by the first user to the supplemental prompts and the first response record, refining includes training the first machine learning model to customize outputs generated by the model for the first user; provide the refined machine learning model for use by the first user; and/or other exemplary implementations.