LLM Output Subportion Modification via Extraction
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Solution Overview
Problem
Users are unable to efficiently modify specific parts of large language model (LLM) outputs while leaving other parts unchanged, requiring them to copy and paste the entire output for editing.
Innovation Solution
Implementing a system that allows users to select and modify specific subportions of LLM outputs by highlighting content or issuing verbal requests, without requiring the entire output to be copied and edited in a separate application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the entire LLM output is copied to a text editor for modification, then the user can edit any part of the output, but the computational resources and time required increase significantly
Solution Approach 1:
The system extracts only the selected subportion of the LLM output that the user wants to modify, rather than requiring the user to copy the entire output. This is achieved by detecting the user's selection (via highlighting or verbal request) and extracting only that specific segment for processing in the text editor, thereby reducing the time and effort required.
Solution Approach 2:
The LLM output is segmented into selectable subportions that can be independently manipulated. The system allows users to select specific segments (paragraphs, sentences, or words) and process only those segments through the text editor, rather than treating the entire output as a single unit that must be copied and edited in its entirety.
2Adaptability or versatility
If the entire LLM output is processed through the LLM for modification, then comprehensive changes can be made, but computational resource consumption increases
Solution Approach 1:
The system extracts only the selected subportion of the LLM output for reprocessing through the LLM or text editor, rather than processing the entire output. This extraction approach ensures that computational resources are consumed only for the necessary modifications, significantly reducing energy and computational resource requirements while maintaining the ability to make comprehensive changes to the selected segment.
Solution Approach 2:
Instead of processing the entire LLM output through the computationally expensive LLM model, the system applies partial action by processing only the selected subportion. This may involve using lighter-weight models or simpler text editing operations on the extracted segment, thereby reducing computational resource consumption while still achieving the desired modification versatility.
3Ease of manufacture
If manual copying and pasting is required for editing, then modifications can be made externally, but user convenience decreases
Solution Approach 1:
The system merges the LLM output display with the text editing functionality, allowing users to select and edit portions of the output directly within the same interface. This integration eliminates the need for separate copy-paste operations between applications, thereby maintaining external editing capability while significantly improving user convenience.
Solution Approach 2:
The system introduces an intermediary selection mechanism that bridges the LLM output and the text editor. When users select a subportion (via highlighting or verbal request), the system automatically extracts and transfers only that selected content to the text editor for editing, eliminating manual copy-pasting while preserving the ability to edit externally.
4Manufacturing precision
If the full LLM output is reprocessed for modification requests, then accurate changes can be made, but processing time increases
Solution Approach 1:
The system extracts only the selected subportion of the LLM output for reprocessing, rather than reprocessing the entire output. This extraction maintains manufacturing precision because the LLM or text editor processes only the relevant segment with the modification request, ensuring accurate changes are made to the selected content without the time penalty of processing the full output.
Solution Approach 2:
The modification process is segmented to apply only to the selected subportion of the output. By dividing the output into selectable segments and processing only the relevant segment in response to user requests, the system achieves accurate modifications (manufacturing precision) while significantly reducing the processing time required compared to reprocessing the entire output.
Data Source
AI summary
Implementations are described herein for using LLMs to modify less than the entirety of rendered LLM outputs. In various implementations, a first LLM response is used by a client application to provide first rendered LLM output. The client application may provide (i) an indication of a subportion of the first rendered LLM output that is selected by a user, and (ii) a request to modify the selected subportion. A subportion of the first LLM response corresponding to the selected subportion of the first rendered LLM output may be used to assemble a second LLM prompt, which may be processed using one or more LLMs to generate a second LLM response. The second LLM response may be operable to provide second rendered LLM output that includes at least part of the first rendered LLM output outside of the selected subportion and the modified selected subportion.