Session-Aware LLM Output Adaptation Using User Engagement Signals
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Solution Overview
Problem
Large language models (LLMs) often provide responses that are either too complex or too basic for a user's knowledge level, leading to user frustration and disengagement.
Innovation Solution
The system tracks user engagement with LLM output, such as mouse movements or gaze, to capture additional information about elements of interest, which is used to generate tailored subsequent inputs for the LLM, allowing it to provide responses better suited to the user's expertise level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LLM provides detailed and comprehensive responses, then information completeness is improved, but user understanding becomes difficult when knowledge level is mismatched
Solution Approach 1:
The system dynamically adjusts the complexity and detail level of LLM responses based on real-time detection of user engagement patterns. When users interact with explanation elements or exhibit confusion signals, the system modifies subsequent responses to be more tailored to their demonstrated knowledge level, transforming static responses into adaptive, dynamic content delivery.
Solution Approach 2:
The system implements feedback loops by monitoring user engagement metrics (hover time, clicks on explanation elements, scroll behavior) and using this feedback to adjust future response characteristics. This closed-loop system continuously refines response complexity based on actual user comprehension signals, ensuring information remains both complete and accessible.
2Ease of operation
If LLM provides simplified responses, then user understanding is improved, but information completeness may be reduced
Solution Approach 1:
The system segments information delivery into multiple layers: core content, explanatory elements, and optional detailed expansions. Users receive simplified primary responses for immediate understanding, with additional comprehensive information available through interactive elements they can access when needed, thus maintaining both simplicity and completeness.
Solution Approach 2:
Different portions of the response are optimized for different purposes: key conclusions are presented in simplified language for immediate comprehension, while technical details and comprehensive explanations are provided in expandable sections or supplementary materials that maintain full information depth for users who need it.
3Productivity
If LLM does not adapt to user knowledge level, then response generation speed is maintained, but user engagement and satisfaction deteriorate
Solution Approach 1:
The system performs preliminary analysis of user engagement patterns during the initial interaction phase, detecting knowledge level indicators before generating subsequent responses. This advance detection allows the system to pre-adjust response characteristics, maintaining rapid response generation while progressively improving engagement through personalized content delivery.
4Measurement precision
If LLM provides technical terminology without explanation, then precision is improved, but user comprehension becomes difficult
Solution Approach 1:
The system introduces explanatory intermediaries that bridge technical terminology and user comprehension. When users encounter unfamiliar terms, the system automatically provides contextual explanations, definitions, or analogies that maintain the precision of technical concepts while making them accessible to users with varying knowledge levels.
Data Source
AI summary
Implementations are described herein for using the information about user engagement with large language model (LLM) output as a subsequent input into an LLM, so that the LLM is able to provide, for rendition on one or more output devices, a subsequent output that is tailored to the user. In various implementations, based on one or more input device signals, a user engagement event with an element of a first LLM output generated using a LLM and rendered using one or more output devices may be detected. Additional information about the element of the first LLM output may be captured and used to generate at least part of a subsequent input prompt for the LLM. The subsequent input prompt may be processed using the LLM to generate a subsequent LLM output for rendition on one or more of the output devices.