Editable Style Guides for User-Controlled Neural Text Generation
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Solution Overview
Problem
Existing Large Language Models (LLMs) lack personalization and user control, leading to unsatisfactory results and increased time and processing resources in collaborative writing tasks due to their inability to adapt to a user's evolving personal style.
Innovation Solution
A method for style guide management using a generative neural network model that allows users to input a writing sample, generate a style guide, modify it based on explicit preferences, and receive personalized text generation outputs through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing LLM-powered writing systems are used, then text generation capability is provided, but user control and personalization are insufficient leading to unsatisfactory results
Solution Approach 1:
The system implements feedback by capturing user edits to generated text and using these edits to refine the style guide. Users can modify generated content and the system learns from these modifications to improve future generation quality, creating a closed-loop feedback mechanism that enhances both user control and output reliability
Solution Approach 2:
The system enables self-service by allowing users to directly edit and modify the style guide without requiring technical expertise. Users can personalize the style guide according to their preferences and the system automatically applies these customizations, making the system adaptable to individual user needs while maintaining ease of operation
2Adaptability or versatility
If style systems focus on learning semantics within existing text, then style generation is achieved, but user involvement in developing style is excluded
Solution Approach 1:
The system implements dynamics by making the style guide editable and adaptable to user preferences. Rather than a static style system, the style guide can be dynamically modified by users based on their evolving needs, allowing the system to adapt to changing user requirements while maintaining ease of operation through intuitive editing interfaces
3Productivity
If LLMs are used for collaborative writing, then writing assistance is provided, but time and processing resources increase due to lack of personalization
Solution Approach 1:
The system applies preliminary action by pre-configuring a style guide that captures user preferences and editing patterns before the actual writing task. This pre-personalization allows the LLM to generate text more efficiently without requiring extensive real-time adjustments, reducing processing time while maintaining high writing efficiency through customized generation parameters
Data Source
AI summary
A method for style guide management is described. A first user input is received from a user via a graphical user interface (GUI). The first user input identifies a writing sample having a textual style. A style guide is generated, based on the writing sample, having a description of a target style, based on the textual style, for input to a generative neural network model (GNNM). A profile representing the style guide and comprising a natural language format description is sent for display in the GUI. The style guide is modified based on an explicit indication of a style preference. A request for drafting assistance is sent to the GNNM, the request including the style guide for text generation according to the style guide by the GNNM. An output generated by the GNNM in response to the request is obtained. The output is sent to be displayed within the GUI.