Text Generation Model With Sentence-Count Guided Length Control
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Solution Overview
Problem
Existing text generation models often produce inaccurate or unnatural output text when length control is applied, leading to inconsistencies in sentence count and token count.
Innovation Solution
A text processing model is trained using machine learning techniques to control output text by incorporating sentence count and token count labels, with special characters indicating sentence number and token positions, and adjusted through loss data to generate accurate and natural phrasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If output text is limited by length control, then text length is controlled, but output text accuracy and naturalness deteriorate
Solution Approach 1:
The patent implements feedback mechanisms by introducing sentence count labels and token position labels that provide continuous guidance to the text generation model. The model receives feedback about the current sentence count and token positions, allowing it to adjust its generation process to meet length constraints while maintaining accuracy and naturalness through iterative refinement based on label comparisons.
Solution Approach 2:
The patent changes key parameters by introducing sentence count as a controllable parameter and token position labels that track generation progress. These parameter changes enable the model to dynamically adjust its output to satisfy length constraints while preserving text quality through structured control mechanisms.
2Length of moving object
If output text is limited by length control, then text length is controlled, but natural language patterns deteriorate
Solution Approach 1:
The patent introduces token position labels as intermediary elements that mediate between the length control requirements and the natural language generation process. These labels act as a bridge, providing structured guidance that maintains natural phrasing while enforcing length constraints through intermediate representation rather than direct constraint imposition.
Solution Approach 2:
The patent applies preliminary action by pre-defining sentence count targets and token position labels before text generation begins. This allows the model to plan its output structure in advance, ensuring natural language patterns are maintained while length constraints are satisfied through预先 structured guidance.
3Measurement precision
If sentence count and token count labels are used, then text generation accuracy is improved, but model complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the text generation control into discrete components: sentence count labels for high-level structure and token position labels for fine-grained control. This segmentation allows the model to manage complexity through modular label processing while achieving precise measurement and control of output text properties.
Data Source
AI summary
The disclosure herein describes training a text processing model to generate model output text data using input text data and a sentence count. A training data entry including input text data and output text data is obtained. A sentence count of the output text data is determined, and the output text data is labeled with a sentence count label and a sentence number label. Model output text data is generated with a text processing model using the input text data and determined sentence count as input data. Loss data associated with a difference between the generated model output text data and the labeled output text data is determined and the text processing model is adjusted using the determined loss data. The use of labeled output text data enables the model to be trained to produce output text data with a target sentence count in a computationally efficient manner.