LLM Message Feature Sets for Audience-Presenter Alignment
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Solution Overview
Problem
Large language models (LLMs) struggle with contextual limitations in generating nuanced and context-specific output, often requiring user input that is suboptimal due to lack of access to relevant information and limited feedback, leading to ineffective message engagement.
Innovation Solution
A system that processes information on target groups, presenters, and message content using machine learning models to generate feature sets that optimize input to LLMs, selecting suitable presenters and tailoring messages for effective delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If limited feedback is provided to the LLM, then the input processing is simpler and faster, but the output quality and engagement effectiveness deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically gathering relevant context information from multiple sources (user profiles, interaction history, document data) and formatting it into structured feature sets before inputting to the LLM. This pre-processing ensures the LLM receives comprehensive context without requiring lengthy user feedback, thus maintaining both processing efficiency and output quality.
Solution Approach 2:
The system introduces an intermediary layer between the user and the LLM that automatically enriches the input with contextual information. This intermediary component (the message optimization system) gathers data from multiple sources, processes it through machine learning models, and presents it to the LLM in an optimized format, eliminating the need for users to manually provide detailed feedback while ensuring high-quality output.
2Reliability
If comprehensive feature sets are generated and input to the LLM, then the message effectiveness and engagement are improved, but the system complexity increases
Solution Approach 1:
The system segments the complex task of message optimization into distinct functional modules: user profile analysis, interaction history processing, document data retrieval, machine learning-based feature extraction, and LLM input generation. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while delivering comprehensive message optimization.
Solution Approach 2:
The system creates a universal message optimization framework that can handle multiple types of inputs (user profiles, interaction history, document data) and generate optimized messages for various contexts. The machine learning models and feature generation mechanisms are designed to be adaptable across different scenarios, reducing the need for separate specialized systems.
3Adaptability or versatility
If user profiles and interaction history are analyzed to determine information preferences, then the message personalization is improved, but the data processing time increases
Solution Approach 1:
The system performs preliminary analysis of user profiles and interaction history to extract and store key preference patterns in advance. By pre-processing this data and maintaining updated user preference models, the system can quickly retrieve and apply personalized information preferences when generating optimized messages, rather than analyzing raw data from scratch each time.
Solution Approach 2:
The system transforms detailed user profile data and interaction history into condensed preference parameters and feature representations. By converting raw data into optimized parameter sets that capture essential user preferences, the system reduces the computational burden of personalization while maintaining high adaptability and message relevance.
Data Source
AI summary
Provided are a computer program product, system, and method for generating feature sets to input to a large language model to optimize a message. A source message is inputted to a first machine learning model to determine topics in the source message. Information type preferences of the members of the target group, the topics in the source message, and skillsets of the presenters correlated with the topics in the source message are inputted to a second machine learning model to output performance scores for the presenters predicting a suitability of the presenters to deliver the source message. The source message, the topics in the source message, the skillsets of a selected presenter, having a performance score exceeding a threshold, correlated with the topics, and the information type preferences of the members of the target group are inputted to an LLM to output a target message to the target group.