Length-Controlled Text Generation With Sentence-Count Feedback
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Solution Overview
Problem
Existing text generation models struggle to accurately control the length of output text, often resulting in inaccurate or unnatural phrasing when constrained.
Innovation Solution
A text processing model is trained using machine learning techniques to incorporate sentence or token counts, adjusting its performance based on loss data to generate output text with controlled length and natural phrasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If text generation models apply length constraints to control output text length, then the text length control is improved, but the text accuracy and naturalness deteriorate
Solution Approach 1:
The patent introduces a feedback mechanism where the model predicts the length of generated text and compares it with the target length. The prediction error is fed back to adjust the generation process, enabling dynamic length control without compromising text quality. This is achieved through length prediction modules that continuously monitor and adjust the generation trajectory.
Solution Approach 2:
The patent performs preliminary length prediction before completing the full text generation. By estimating the output length early in the generation process and adjusting accordingly, the model can control the final text length while maintaining natural phrasing. This preliminary action allows the model to plan the generation trajectory to meet length constraints.
2Loss of information
If text generation models increase output text length, then the information completeness is improved, but the resource consumption increases
Solution Approach 1:
The patent employs dynamic length control where the model can adjust the output length based on the complexity and information density of the input text. The length prediction and control mechanisms allow the model to generate sufficiently long text to preserve information while avoiding unnecessary verbosity that would waste computational resources.
3Use of energy by moving object
If text generation models decrease output text length, then the resource consumption is reduced, but the information completeness deteriorates
Solution Approach 1:
The patent changes the parameter of output length dynamically based on the specific generation task and input characteristics. Rather than using a fixed length constraint, the model adjusts the target length parameter to achieve the optimal balance between information completeness and resource efficiency for each generation task.
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 used to adjust the text processing model, which enables the model to be trained to produce output text data with a target sentence count in a computationally efficient manner.