LLM Document Style Conformance via Engineered Prompts
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Solution Overview
Problem
Current large language models (LLMs) struggle with consistently following fine-grained text editing instructions, particularly in complex scenarios involving multiple instructions and long input texts.
Innovation Solution
The development of a computer system and method that utilizes a benchmark suite to evaluate LLMs' performance in following instructions, with further instruction tuning on text-editing data to improve performance, and the use of engineered prompts to guide LLMs in transforming electronic documents to conform to specific style guides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used to follow fine-grained text editing instructions, then text transformation capability is improved, but instruction-following consistency deteriorates
Solution Approach 1:
The patent segments the instruction-following process into distinct components: instruction parsing, constraint identification, transformation planning, and execution verification. This segmentation allows each component to be optimized independently, improving overall reliability while maintaining versatility in text transformation tasks.
Solution Approach 2:
The patent implements feedback mechanisms where the LLM's output is evaluated against the original instructions, and correction loops are introduced to ensure instruction-following consistency. This feedback system maintains reliability by continuously verifying that transformations adhere to the specified constraints.
2Adaptability or versatility
If LLMs handle multiple instructions simultaneously, then task complexity is improved, but performance accuracy deteriorates
Solution Approach 1:
The patent applies segmentation by breaking down multiple simultaneous instructions into individual processing units. Each instruction is parsed, validated, and executed in a structured sequence, allowing the system to handle complex multi-instruction tasks while maintaining high accuracy through systematic processing of each component.
Solution Approach 2:
The patent employs preliminary action by pre-processing and organizing multiple instructions before execution. Instructions are prioritized, conflicts are resolved in advance, and a execution plan is generated that ensures accurate fulfillment of all instructions while managing task complexity effectively.
3Adaptability or versatility
If LLMs process long input texts, then document transformation capability is improved, but instruction-following reliability deteriorates
Solution Approach 1:
The patent segments long input texts into manageable chunks or sections that can be processed individually while maintaining context awareness. This segmentation approach enables the LLM to handle lengthy documents effectively while preserving instruction-following reliability through systematic processing of each segment with reference to the overall instructions.
Data Source
AI summary
In an embodiment, non-transitory computer-readable storage media store one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute: executing a document processing application; receiving a digitally stored electronic document, alone or in combination with one or more other relevant documents, and an engineered prompt; transmitting an application programming interface (API) call to an API of a pre-trained large language model (LLM), wherein the call comprises the engineered prompt, wherein the engineered prompt comprises a plurality of objective instructions to the pre-trained LLM specifying transforming the electronic document according to a style guide to cause the pre-trained LLM to execute an inference stage over the electronic document and automatically generate output text based on the electronic document and the plurality of objective instructions that transforms the electronic document to conform to the style guide; storing the output text using a storage device of a user computer, a hosted storage environment, or in memory associated with the document processing application.