Language Model Writing Aid System for Content Generation
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Solution Overview
Problem
Current computer-aided writing technologies cannot assist authors in writing creation, limiting their ability to generate new content beyond optimization and information retrieval.
Innovation Solution
A language model-based writing aid method and system that acquires original text, inputs it into a trained language model to generate multiple pieces of writing text, allowing for iterative extension and modification, enabling the system to aid authors in writing creation by generating diverse and logical content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computer aided writing technology is used to optimize writing contents, then writing quality can be improved through word recommendation and statement polishing, but the system cannot assist authors in writing creation or generate new content
Solution Approach 1:
The patent replaces traditional rule-based writing optimization systems with a neural network-based language model. The neural network learns linguistic patterns and generation capabilities from training data, enabling it to create new content rather than just optimize existing text. This substitution of mechanical rule-based systems with learning-based systems provides the breakthrough in writing creation capability.
Solution Approach 2:
The patent changes the fundamental parameters of the writing aid system by introducing a language model with configurable generation parameters such as temperature, top-k, and top-p. These parameter changes allow the system to control the creativity and diversity of generated text, enabling writing creation while maintaining manageable system complexity through parameterized control.
2Adaptability or versatility
If a language model generates multiple pieces of writing text to provide diversity, then content variety improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the language model on extensive corpora before deployment. This pre-training establishes the foundational knowledge and generation capabilities, allowing the model to produce diverse content during inference without requiring extensive processing time for each generation task. The heavy computational work is done beforehand.
Solution Approach 2:
The patent generates a preset number of writing text pieces (excessive action) to ensure sufficient diversity is achieved. By generating more options than may ultimately be needed, the system guarantees content variety while allowing users to select from multiple candidates, thereby achieving diversity without requiring unlimited generation iterations.
3Ease of operation
If the system generates writing text based on user input and existing text, then writing assistance capability improves, but the complexity of text processing and model integration increases
Solution Approach 1:
The patent implements a universal language model that can handle multiple writing assistance tasks through a single system. The model processes both user input text and existing writing text using the same architectural framework, providing versatile writing assistance without requiring separate specialized systems for different text processing functions.
Solution Approach 2:
The patent introduces an intermediary processing layer that manages the integration between user input, existing text, and language model generation. This intermediary layer handles text preprocessing, model inference coordination, and output postprocessing, thereby managing the complexity of text processing while maintaining ease of operation for users.
Data Source
AI summary
The present disclosure provides a language model based writing aid method, apparatus and system. The method includes: a server acquiring original text, where the original text may be writing text already generated and/or user input text; the server inputting the original text into a language model to generate a preset number of pieces of writing text, where the writing text and the original text have a correlation; the server sending the preset number of pieces of writing text to a frontend interface. The method of the present disclosure enables a computer to aide a user in text creating so that intelligence for writing aid is improved.