LLM Question Recommendation System for User Query Context
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Solution Overview
Problem
Existing technologies lack an efficient method for recommending questions that are associated with content from information providers as answers to user queries, particularly in large language models (LLM) systems.
Innovation Solution
A method and system that utilize a large language model (LLM) to create question recommendations by generating prompts, creating multiple question candidates, and selecting questions based on correlation with LLM results, user characteristics, and conversation context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple question candidates are generated using LLM, then question recommendation quality is improved, but system complexity increases
Solution Approach 1:
The system segments the question generation process into distinct stages: LLM generates multiple question candidates from the prompt, then a selection mechanism filters these candidates based on relevance to information provider content. This segmentation allows complex multi-candidate generation to be managed through modular processing steps, resolving the contradiction between quality improvement and system complexity.
Solution Approach 2:
The system performs preliminary action by pre-generating multiple question candidates using the LLM before final selection. This preliminary generation creates a pool of options that can be evaluated and selected based on specific criteria, improving recommendation quality while maintaining manageable complexity through advance preparation.
2Measurement precision
If correlation-based selection is used for question candidates, then recommendation accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial action by calculating correlation metrics for only the most promising question candidates rather than all possible candidates. This selective evaluation maintains high recommendation accuracy while reducing processing time by focusing computational resources on the most relevant options.
Solution Approach 2:
The system changes parameters by adjusting the correlation threshold and selection criteria dynamically. By modifying these parameters, the system can balance between accuracy and processing time based on specific operational requirements, resolving the contradiction between precise recommendation and fast processing.
3Adaptability or versatility
If dynamic content creation is performed for selected questions, then user engagement is improved, but computational load increases
Solution Approach 1:
The system performs preliminary action by pre-creating or pre-processing content templates that can be dynamically instantiated when questions are selected. This advance preparation reduces the computational load during actual user interactions while maintaining high adaptability and engagement through personalized content delivery.
Solution Approach 2:
The system uses copying by creating dynamic content instances from pre-defined templates or existing information provider content. Instead of generating entirely new content computationally, the system copies and adapts existing content structures, reducing computational load while maintaining user engagement through customized content delivery.
Data Source
AI summary
A method and a system for recommending a question associated with content of an information provider as an answer to a user query. A question recommendation method may include creating LLM results based on a large language model (LLM) for a prompt of a user; creating a question for association with content of an information provider based on the LLM results; and providing the created question as a response to the prompt of the user with the created LLM results.