Annotated LLM Prompting for Consistent Multi-Instruction Text Revision
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Solution Overview
Problem
Existing ML-based text revision services limit user input to general instructions, process each editing instruction in isolation, leading to inefficiencies and inconsistencies, and do not allow for specific stylistic instructions, resulting in poor model performance and inconsistent text revisions.
Innovation Solution
A technical solution that enables multiple text-editing instructions to be included in a single prompt to a large language model (LLM), providing contextual information and improving computational efficiency, cohesiveness, and relevance of the generated text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple editing instructions are processed in isolation by ML-based services, then each instruction can be handled individually, but computational efficiency deteriorates and inconsistencies arise in the revised text
Solution Approach 1:
The patent combines multiple editing instructions into a single prompt that is processed by the large language model in one operation. This merging approach eliminates the need to process instructions sequentially in isolation, thereby improving computational efficiency while ensuring that all instructions are applied consistently to generate a cohesive revised text output.
2Adaptability or versatility
If general instructions are provided for text revision, then the system can process a broader range of editing tasks, but the ability to provide specific stylistic instructions is lost
Solution Approach 1:
The patent enables users to provide specific stylistic instructions for different portions of text while maintaining the ability to handle various editing tasks. The system accepts both general editing instructions and specific stylistic directives, allowing users to tailor the revision process to their needs with greater precision and ease of operation.
3Manufacturing precision
If multiple prompts are processed sequentially, then each instruction can be applied step-by-step, but processing time increases and model performance deteriorates
Solution Approach 1:
The patent merges multiple editing instructions into a single comprehensive prompt that is processed by the large language model in one operation. This approach reduces processing time significantly compared to sequential processing of multiple prompts, while the model's ability to understand and apply all instructions simultaneously ensures high quality text revision output.
4Ease of operation
If the user interface provides limited editing options, then the system is easier to operate, but the intuitiveness and user satisfaction deteriorate
Solution Approach 1:
The patent implements a user interface that provides versatile editing options while maintaining ease of operation. The system offers multiple editing instructions and stylistic options that users can select from, making the interface intuitive and adaptable to different user needs, while still being simple enough for easy operation.
Data Source
AI summary
Methods and systems for prompting a large language model (LLM) to process inputs from multiple user elements to generate a revised block of text are described. One or more text-editing instructions related to respective one or more selected text portions in a block of text are received. A prompt is generated for a LLM to generate a revised block of text, the prompt including at least a portion of an annotated block of text, the annotated block of text including each text-editing instruction inserted into the block of text relative to each respective selected text portion. The prompt is provided to the LLM and a revised block of text is received and outputted.