Long-Form Text Schema With Iterative Feedback for Narrative Coherence
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Solution Overview
Problem
Conventional AI systems struggle with generating long-form narrative text due to continuity challenges, resulting in incohesive and disjointed storylines that are hard to follow.
Innovation Solution
A system that uses a declarative and machine-readable data format to generate long-form text, incorporating a specification framework with iterative user feedback to ensure continuity, using a large language model to populate the narrative with structured suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional AI systems generate long-form narrative text, then text generation capability is demonstrated, but continuity and coherence deteriorate
Solution Approach 1:
The patent segments the long-form text generation process into multiple chapters, with each chapter generated separately using the same prompt. This segmentation allows the AI to maintain continuity within each chapter while generating manageable portions of the overall narrative, thereby improving coherence without sacrificing productivity
Solution Approach 2:
The patent employs preliminary action by first generating an outline or table of contents before creating the full narrative. This preliminary structure provides a framework that guides subsequent chapter generation, ensuring continuity and coherence across the entire long-form text while maintaining efficient generation throughput
2Speed
If AI generates complete long-form text in one pass, then generation speed is maintained, but quality and followability deteriorate
Solution Approach 1:
The patent divides the text generation into sequential chapters rather than attempting to generate the entire work in one pass. Each chapter is generated with the same prompt, maintaining consistent quality standards while preserving overall generation speed through efficient batch processing of segmented portions
Solution Approach 2:
The patent incorporates feedback mechanisms where each generated chapter is evaluated against continuity requirements before proceeding to the next chapter. This iterative feedback process ensures storyline quality and coherence without significantly impacting the overall generation timeline
Data Source
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AI summary
The description relates to computer-assisted generation of long-form text by creating a schema that includes a declarative and machine-readable data format. Based on the schema, processes iteratively generate suggested code to populate a specification that provides the narrative framework for the long-form text. The specification includes structure and substance for inclusion in the long-form text. The interactive nature of the specification development allows a user to progressively update and confirm automatically generated suggestions. In this manner, the specification is updated according to approved code selected from the iteratively generated code. Additional processes serialize the specification to generate multiple unit specifications. A large language model (LLM) is used to generate the long-form text based on the unit specifications.