LLM Text Editing Framework for Human-Like Output Alignment
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Solution Overview
Problem
Existing large language models (LLMs) generate text with quality limitations, even when refined through reinforcement learning from human feedback (RLHF), as they often produce outputs with idiosyncrasies and flaws that do not align well with human-generated texts.
Innovation Solution
Implement a human-feedback based framework for editing LLM-generated text, using a comprehensive taxonomy of edit categories derived from expert writing practices, and a secondary LLM moderator to refine outputs, ensuring ethical guidelines and factual accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning from human feedback (RLHF) is used to refine LLM outputs, then ethical guidelines and safety are improved, but text quality and alignment with human-generated text deteriorate due to persistent idiosyncrasies and flaws
Solution Approach 1:
The patent divides the text refinement process into multiple independent modules: an LLM generator produces initial text, a detector identifies problematic spans with categorization, and an editor applies targeted edits. This segmentation allows each component to specialize, improving overall text quality without compromising ethical adherence.
Solution Approach 2:
The patent introduces a detector-editor intermediary system between the LLM generator and final output. The detector acts as a mediator that identifies issues and categorizes them, then guides the editor to apply appropriate fixes. This intermediary layer resolves the contradiction by bridging ethical constraints and quality improvements.
2Manufacturing precision
If a comprehensive editing framework with multiple categories is implemented, then text quality and alignment are improved, but system complexity increases
Solution Approach 1:
The editing framework is segmented into distinct functional components: detection module, categorization module with seven specific edit categories, and execution module. This segmentation makes the complex system manageable by dividing it into independent, well-defined parts that can be developed and maintained separately.
Solution Approach 2:
The patent changes the parameter of edit categorization from a single undifferentiated category to seven specific categories (factual accuracy, ethical guideline violation, grammar/spelling, style/tone, redundancy, clarity, and completeness). This parameter change organizes complexity into structured, manageable dimensions.
3Manufacturing precision
If multiple edit categories and expert writing practices are incorporated, then alignment with human-generated text is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing edits only on detected problematic spans rather than processing the entire text uniformly. The detector identifies specific regions needing improvement, and the editor applies corrections only to those areas, reducing overall processing time while maintaining high alignment quality.
Solution Approach 2:
The editing framework applies local quality by using seven different edit categories that target specific aspects of text quality in different regions. Each problematic span receives customized editing based on its specific issue type, rather than applying a uniform editing approach throughout the entire text.
Data Source
AI summary
Embodiments described herein provide a method of detecting whether an input text is AI-generated using a neural network based language model. The method may include: formulating a span detection prompt including the input text and examples of problematic texts; generating, using the neural network based large language model in response to the span detection prompt, a textual spans in the input text and a category for each textual span in the plurality of textual spans; formulating an edit category prompt including the plurality of textual spans, the category for each textual span, and a plurality of example edits for each category; generating, using the neural network based large language model in response to the edit category prompt, a plurality of edited textual spans associated with the category; generating a revised sample text from the edits of the plurality of textual spans; and outputting, to a display, the revised sample text.