Generative Response Simplification for User-Requested AI Explanations
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Solution Overview
Problem
Users struggle to understand complex responses generated by generative models due to insufficiently descriptive or abundant words, and the content requested may not be readily available, especially when expressing imagined concepts without clear ways to do so.
Innovation Solution
A computing system with processors receives user queries, generates model inputs for generative models, and provides simplified explanations through user-selectable interface elements, leveraging classification and generative models to break down complex topics into structured multi-part responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If generative models produce detailed responses to user queries, then information completeness is improved, but user comprehension becomes difficult due to complexity
Solution Approach 1:
The system segments the detailed generative model response into multiple simplified summaries at different levels of detail. Users can select their preferred summary length (e.g., brief, medium, detailed), allowing them to comprehend the information at their own pace while still accessing complete information when needed.
Solution Approach 2:
The system changes the parameter of information presentation by generating multiple versions of the same content with varying levels of abstraction and detail. The generative model produces responses parameterized by summary length, enabling users to switch between comprehensive details and simplified overviews based on their comprehension needs.
2Ease of operation
If the system generates simplified explanations for all responses, then user comprehension is improved, but computational resources are wasted on unnecessary simplifications
Solution Approach 1:
The system applies partial action by generating simplified summaries only when users explicitly request them or when the query type indicates complexity that would benefit from summarization. For simple queries, the system provides direct answers without invoking the computationally expensive simplification process, thus avoiding unnecessary resource consumption.
Solution Approach 2:
The system dynamically adjusts its response generation strategy based on query analysis and user preferences. It determines whether simplification is needed on a per-query basis, making the computational resource usage adaptive rather than static, thereby avoiding waste on queries that don't require simplification.
3Measurement precision
If users provide more descriptive words and abundant content in queries, then search result quality is improved, but query formulation becomes more difficult
Solution Approach 1:
The system introduces an intermediary layer between the user's simple query and the search process. This intermediary (the generative model) automatically expands and refines the user's query into more descriptive search terms and concepts, eliminating the need for users to manually craft complex, descriptive queries while still achieving high search result quality.
Solution Approach 2:
The system performs preliminary action by pre-processing and expanding user queries before searching. It automatically generates enhanced query versions with additional descriptive elements and related concepts, so users don't need to invest effort in query formulation while the system prepares optimized search inputs in advance.
Data Source
AI summary
The present disclosure provides methods, systems, and devices for providing simplified versions of model responses. A computing system receives a user query. The computing system generates a first model input to a generative model based on the user query. The computing system receives a first model output from the generative model. The computing system transmits the first model output for display to a user in a user interface. The computing system receives a simplification request associated with the first model output. The computing system generates a second model input, the second model input including one or more instructions to provide a simplified explanation of the first model input. The computing system receives a second model output from the generative model, the second model output comprising a simplified version of the first model output. The computing system transmits the second model output for display to a user.